The prevalence and impact of toxic discussions online have made content moderation crucial. Automated systems can play a vital role in identifying toxicity, and reducing the reliance on human moderation. Nevertheless, identifying toxic comments for diverse communities continues to present challenges that are addressed in this paper. The two-part goal of this study is to (1) identify intuitive variances from annotator disagreement using quantitative analysis and (2) model the subjectivity of these viewpoints. To achieve our goal, we published a new dataset[<https://github.com/XXX>] with expert annotators' annotations and used two other public datasets to identify the subjectivity of toxicity. Then leveraging the Large Language Model (LLM), we evaluate the model’s ability to mimic diverse viewpoints on toxicity by varying the size of the training data and utilizing the same set of annotators as the test set used during model training and a separate set of annotators as the test set.
We conclude that subjectivity is evident across all annotator groups, demonstrating the shortcomings of majority-rule voting. Moving forward, subjective annotations should serve as ground truth labels for training models for domains like toxicity in diverse communities.

[1]
Shruthi Mohan, Apala Guha, Michael Harris, Fred Popowich, Ashley Schuster, and Chris Priebe.
The impact of toxic language on the health of reddit communities.
In Advances in Artificial Intelligence: 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Edmonton, AB, Canada, May 16-19, 2017, Proceedings 30, pages 51–56. Springer, 2017.

[2]
Ellery Wulczyn, Nithum Thain, and Lucas Dixon.
Ex machina: Personal attacks seen at scale.
In Proceedings of the 26th international conference on world wide web, pages 1391–1399, 2017.

[3]
Rui Zhao, Anna Zhou, and Kezhi Mao.
Automatic detection of cyberbullying on social networks based on bullying features.
In Proceedings of the 17th international conference on distributed computing and networking, pages 1–6, 2016.

[4]
Nitesh Goyal, Ian D Kivlichan, Rachel Rosen, and Lucy Vasserman.
Is your toxicity my toxicity? exploring the impact of rater identity on toxicity annotation.
Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2):1–28, 2022.

[5]
Kofi Arhin, Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, and Moninder Singh.
Ground-truth, whose truth?–examining the challenges with annotating toxic text datasets.
arXiv preprint arXiv:2112.03529, 2021.

[6]
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber.
Automated hate speech detection and the problem of offensive language.
In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515, 2017.

[7]
Hamdy Mubarak, Kareem Darwish, and Walid Magdy.
Abusive language detection on arabic social media.
In Proceedings of the first workshop on abusive language online, pages 52–56, 2017.

[8]
Michael Wiegand, Melanie Siegel, and Josef Ruppenhofer.
Overview of the germeval 2018 shared task on the identification of offensive language.

[9]
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar.
Predicting the type and target of offensive posts in social media.
arXiv preprint arXiv:1902.09666, 2019.

[10]
Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, and Sara Tonelli.
Agreeing to disagree: Annotating offensive language datasets with annotators' disagreement.
arXiv preprint arXiv:2109.13563, 2021.

[11]
William A Scott.
Reliability of content analysis: The case of nominal scale coding.
Public opinion quarterly, pages 321–325, 1955.

[12]
Jacob Cohen.
A coefficient of agreement for nominal scales.
Educational and psychological measurement, 20(1):37–46, 1960.

[13]
Sidney Siegel.
Nonparametric statistics.
The American Statistician, 11(3):13–19, 1957.

[14]
Joseph L Fleiss.
Measuring nominal scale agreement among many raters.
Psychological bulletin, 76(5):378, 1971.

[15]
Ted Byrt, Janet Bishop, and John B Carlin.
Bias, prevalence and kappa.
Journal of clinical epidemiology, 46(5):423–429, 1993.

[16]
Klaus Krippendorff.
Computing krippendorff's alpha-reliability.

[17]
Anca Dumitrache, Oana Inel, Lora Aroyo, Benjamin Timmermans, and Chris Welty.
Crowdtruth 2.0: Quality metrics for crowdsourcing with disagreement.
arXiv preprint arXiv:1808.06080, 2018.

[18]
Mitchell L Gordon, Michelle S Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S Bernstein.
Jury learning: Integrating dissenting voices into machine learning models.
In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–19, 2022.

[19]
Ka Wong, Praveen Paritosh, and Lora Aroyo.
Cross-replication reliability–an empirical approach to interpreting inter-rater reliability.
arXiv preprint arXiv:2106.07393, 2021.

[20]
Reuben Binns, Michael Veale, Max Van Kleek, and Nigel Shadbolt.
Like trainer, like bot? inheritance of bias in algorithmic content moderation.
In Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part II 9, pages 405–415. Springer, 2017.

[21]
Björn Ross, Michael Rist, Guillermo Carbonell, Benjamin Cabrera, Nils Kurowsky, and Michael Wojatzki.
Measuring the reliability of hate speech annotations: The case of the european refugee crisis.
arXiv preprint arXiv:1701.08118, 2017.

[22]
Mark Díaz, Ian Kivlichan, Rachel Rosen, Dylan Baker, Razvan Amironesei, Vinodkumar Prabhakaran, and Emily Denton.
Crowdworksheets: Accounting for individual and collective identities underlying crowdsourced dataset annotation.
In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 2342–2351, 2022.

[23]
Tyler McDonnell, Matthew Lease, Mucahid Kutlu, and Tamer Elsayed.
Why is that relevant? collecting annotator rationales for relevance judgments.
In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, volume 4, pages 139–148, 2016.

[24]
Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma.
Investigating the benefits of free-form rationales.
arXiv preprint arXiv:2206.11083, 2022.

[25]
Keming Liu.
Annotation as an index to critical writing.
Urban Education, 41(2):192–207, 2006.

[26]
J Richard Landis and Gary G Koch.
The measurement of observer agreement for categorical data.
biometrics, pages 159–174, 1977.

[27]
Yifan Hu, Yehuda Koren, and Chris Volinsky.
Collaborative filtering for implicit feedback datasets.
In 2008 Eighth IEEE international conference on data mining, pages 263–272. Ieee, 2008.

[28]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.
Language models are few-shot learners.
Advances in neural information processing systems, 33:1877–1901, 2020.

[29]
Brian Lester, Rami Al-Rfou, and Noah Constant.
The power of scale for parameter-efficient prompt tuning.
In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.

[30]
McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang.
What we can't measure, we can't understand: challenges to demographic data procurement in the pursuit of fairness.
In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 249–260, 2021.

[31]
Bo Pang and Lillian Lee.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts.
arXiv preprint cs/0409058, 2004.

[32]
Bing Liu et al.
Sentiment analysis and subjectivity.
Handbook of natural language processing, 2(2010):627–666, 2010.

[33]
Antonio Reyes and Paolo Rosso.
Mining subjective knowledge from customer reviews: A specific case of irony detection.
In Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis (WASSA 2.011), pages 118–124, 2011.

[34]
Julia Hirschberg, Jackson Liscombe, and Jennifer Venditti.
Experiments in emotional speech.
In ISCA & IEEE Workshop on Spontaneous Speech Processing and Recognition, 2003.

[35]
Rada Mihalcea and Hugo Liu.
A corpus-based approach to finding happiness.
In AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pages 139–144, 2006.

[36]
Ebba Cecilia Ovesdotter Alm.
Affect in* text and speech.
University of Illinois at Urbana-Champaign, 2008.

[37]
Hugo Liu, Henry Lieberman, and Ted Selker.
A model of textual affect sensing using real-world knowledge.
In Proceedings of the 8th international conference on Intelligent user interfaces, pages 125–132, 2003.

[38]
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei.
Scaling instruction-finetuned language models, 2022.

[39]
Sid Mittal, Vineet Gupta, Frederick Liu, and Mukund Sundararajan.
Using foundation models to detect policy violations with minimal supervision, 2023.

[1]
Shruthi Mohan, Apala Guha, Michael Harris, Fred Popowich, Ashley Schuster, and Chris Priebe.
The impact of toxic language on the health of reddit communities.
In Advances in Artificial Intelligence: 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Edmonton, AB, Canada, May 16-19, 2017, Proceedings 30, pages 51–56. Springer, 2017.

[2]
Ellery Wulczyn, Nithum Thain, and Lucas Dixon.
Ex machina: Personal attacks seen at scale.
In Proceedings of the 26th international conference on world wide web, pages 1391–1399, 2017.

[3]
Rui Zhao, Anna Zhou, and Kezhi Mao.
Automatic detection of cyberbullying on social networks based on bullying features.
In Proceedings of the 17th international conference on distributed computing and networking, pages 1–6, 2016.

[4]
Nitesh Goyal, Ian D Kivlichan, Rachel Rosen, and Lucy Vasserman.
Is your toxicity my toxicity? exploring the impact of rater identity on toxicity annotation.
Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2):1–28, 2022.

[5]
Kofi Arhin, Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, and Moninder Singh.
Ground-truth, whose truth?–examining the challenges with annotating toxic text datasets.
arXiv preprint arXiv:2112.03529, 2021.

[6]
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber.
Automated hate speech detection and the problem of offensive language.
In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515, 2017.

[7]
Hamdy Mubarak, Kareem Darwish, and Walid Magdy.
Abusive language detection on arabic social media.
In Proceedings of the first workshop on abusive language online, pages 52–56, 2017.

[8]
Michael Wiegand, Melanie Siegel, and Josef Ruppenhofer.
Overview of the germeval 2018 shared task on the identification of offensive language.

[9]
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar.
Predicting the type and target of offensive posts in social media.
arXiv preprint arXiv:1902.09666, 2019.

[10]
Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, and Sara Tonelli.
Agreeing to disagree: Annotating offensive language datasets with annotators' disagreement.
arXiv preprint arXiv:2109.13563, 2021.

[11]
William A Scott.
Reliability of content analysis: The case of nominal scale coding.
Public opinion quarterly, pages 321–325, 1955.

[12]
Jacob Cohen.
A coefficient of agreement for nominal scales.
Educational and psychological measurement, 20(1):37–46, 1960.

[13]
Sidney Siegel.
Nonparametric statistics.
The American Statistician, 11(3):13–19, 1957.

[14]
Joseph L Fleiss.
Measuring nominal scale agreement among many raters.
Psychological bulletin, 76(5):378, 1971.

[15]
Ted Byrt, Janet Bishop, and John B Carlin.
Bias, prevalence and kappa.
Journal of clinical epidemiology, 46(5):423–429, 1993.

[16]
Klaus Krippendorff.
Computing krippendorff's alpha-reliability.

[17]
Anca Dumitrache, Oana Inel, Lora Aroyo, Benjamin Timmermans, and Chris Welty.
Crowdtruth 2.0: Quality metrics for crowdsourcing with disagreement.
arXiv preprint arXiv:1808.06080, 2018.

[18]
Mitchell L Gordon, Michelle S Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S Bernstein.
Jury learning: Integrating dissenting voices into machine learning models.
In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–19, 2022.

[19]
Ka Wong, Praveen Paritosh, and Lora Aroyo.
Cross-replication reliability–an empirical approach to interpreting inter-rater reliability.
arXiv preprint arXiv:2106.07393, 2021.

[20]
Reuben Binns, Michael Veale, Max Van Kleek, and Nigel Shadbolt.
Like trainer, like bot? inheritance of bias in algorithmic content moderation.
In Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part II 9, pages 405–415. Springer, 2017.

[21]
Björn Ross, Michael Rist, Guillermo Carbonell, Benjamin Cabrera, Nils Kurowsky, and Michael Wojatzki.
Measuring the reliability of hate speech annotations: The case of the european refugee crisis.
arXiv preprint arXiv:1701.08118, 2017.

[22]
Mark Díaz, Ian Kivlichan, Rachel Rosen, Dylan Baker, Razvan Amironesei, Vinodkumar Prabhakaran, and Emily Denton.
Crowdworksheets: Accounting for individual and collective identities underlying crowdsourced dataset annotation.
In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 2342–2351, 2022.

[23]
Tyler McDonnell, Matthew Lease, Mucahid Kutlu, and Tamer Elsayed.
Why is that relevant? collecting annotator rationales for relevance judgments.
In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, volume 4, pages 139–148, 2016.

[24]
Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma.
Investigating the benefits of free-form rationales.
arXiv preprint arXiv:2206.11083, 2022.

[25]
Keming Liu.
Annotation as an index to critical writing.
Urban Education, 41(2):192–207, 2006.

[26]
J Richard Landis and Gary G Koch.
The measurement of observer agreement for categorical data.
biometrics, pages 159–174, 1977.

[27]
Yifan Hu, Yehuda Koren, and Chris Volinsky.
Collaborative filtering for implicit feedback datasets.
In 2008 Eighth IEEE international conference on data mining, pages 263–272. Ieee, 2008.

[28]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.
Language models are few-shot learners.
Advances in neural information processing systems, 33:1877–1901, 2020.

[29]
Brian Lester, Rami Al-Rfou, and Noah Constant.
The power of scale for parameter-efficient prompt tuning.
In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.

[30]
McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang.
What we can't measure, we can't understand: challenges to demographic data procurement in the pursuit of fairness.
In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 249–260, 2021.

[31]
Bo Pang and Lillian Lee.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts.
arXiv preprint cs/0409058, 2004.

[32]
Bing Liu et al.
Sentiment analysis and subjectivity.
Handbook of natural language processing, 2(2010):627–666, 2010.

[33]
Antonio Reyes and Paolo Rosso.
Mining subjective knowledge from customer reviews: A specific case of irony detection.
In Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis (WASSA 2.011), pages 118–124, 2011.

[34]
Julia Hirschberg, Jackson Liscombe, and Jennifer Venditti.
Experiments in emotional speech.
In ISCA & IEEE Workshop on Spontaneous Speech Processing and Recognition, 2003.

[35]
Rada Mihalcea and Hugo Liu.
A corpus-based approach to finding happiness.
In AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pages 139–144, 2006.

[36]
Ebba Cecilia Ovesdotter Alm.
Affect in* text and speech.
University of Illinois at Urbana-Champaign, 2008.

[37]
Hugo Liu, Henry Lieberman, and Ted Selker.
A model of textual affect sensing using real-world knowledge.
In Proceedings of the 8th international conference on Intelligent user interfaces, pages 125–132, 2003.

[38]
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei.
Scaling instruction-finetuned language models, 2022.

[39]
Sid Mittal, Vineet Gupta, Frederick Liu, and Mukund Sundararajan.
Using foundation models to detect policy violations with minimal supervision, 2023.

is enabled.

§ PUBLIC DATASET DETAILS

* 2017 dataset: The Civil Comments Dataset consists of approximately 2 million public comments from no longer active commenting platforms. Each comment has crowdsourced labels for  toxicity and toxicity subtypes such as obscene, threat, insult, identity attack, and sexually explicit. To obtain toxicity labels, each comment was shown to up to 10 annotators who were asked to rate the toxicity of each comment. Some comments were annotated by more than 10 annotators. Annotators were also asked to indicate the toxicity sub-types for each comment. This dataset is publicly available and data itself is released under CC0 license.[<https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/data>] This dataset uses the following Likert scale for toxicity and its sub-types:

* Toxicity is measured on a binary scale from where 0 corresponds to Not Toxic or Unsure and 1 corresponds to Toxic or Very Toxic -2 to 1 where -2 = Very Toxic, -1= Toxic, 0 = Hard to Say, and 1 = Not Toxic.

* Toxicity sub-type (e.g. identity attack, insult, obscene, threat) is measured again on a binary scale where 0 corresponds to Not Toxic or Unsure, and 1 corresponds to Toxic, according to the definition of that sub-type. 3-point Likert scale, where -1 = Yes, 0 = Not Sure, 1 = No.

* 2022 Dataset: The Specialized Rater Pools Dataset consists of a total of 25,500 comments from the Civil Comments dataset. The authors of the 2022 dataset worked with two groups of annotators (“specialized rater pools”) from different identity groups, one LGBTQ and one African-American, and one control group from the general US population. They then sampled 8500 comments pertaining to each of the identity groups, measured by the identity labels of each group (control group comments reference neither identity).
All three rater groups received the identical entire collection of 25,500 comments, with 5 annotators from each group allowed to annotate each comment. As a result, in total the complete annotated data including all individual annotations  consists of a total of 382,500 annotations. The dataset is publicly available on Kaggle in CSV and TSV formats as the Jigsaw Specialized Rater Pools Dataset[< https://www.kaggle.com/datasets/google/jigsaw-specialized-rater-pools-dataset>]. This dataset uses a 4-point Likert scale for toxicity, where -2 = Very Toxic, -1 = Toxic, 0 = Unsure, and 1 = Not Toxic, and a 3-point Likert scale for toxicity sub-types, where -1 = Toxic (according to that sub-type), 0 = Unsure, and 1 = Not Toxic.

§ DATA COLLECTION AND ANALYSIS DETAILS

§.§ 2023 dataset (expert annotators) Annotator Details

Annotators were recruited via using email, resulting in a total of 10 annotators, including 4 females and 6 males, all of them residents of the USA. Annotators were compensated by sharing the results of the study, which proved beneficial in enhancing their work. The instructions of the task include disclaimers of toxic comments which are provided to the annotators. Prior to annotation, each participant provided informed consent by signing a consent form. Participants were aware of how the data would be used. The data collection protocol underwent ethical review board approval.

§.§ Toxicity and Toxicity Sub-type Definition

* Toxicity is defined as “a rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion”. This is measured on a 4-point Likert scale with values between -2 and 1, where -2 = Very toxic, -1 = Toxic, 0 = Unsure, and 1 = Not toxic.

* Profanity or Obscenity is defined as “swear words, curse words, or other obscene or profane language”. This is measured on a 3-point Likert scale from -1 to 1.

* Insult is defined as “insulting, inflammatory, or negative comment towards a person or a group of people”. This is measured on a 3-point Likert scale from -1 to 1.

* Threatening describes “an intention to inflict pain, injury, or violence against an individual or group”. This is measured on a 3-point Likert scale from -1 to 1.

* Identity-based negativity is defined as “negative or hateful comments targeting someone because of their identity”. This is measured on a 3-point Likert scale from -1 to 1.

§.§ Dataset transformation for calculating of IRR and xRR

In terms of the 2017 and 2022 datasets, we take into account 25,500 comments which are common in both these datasets. The 2017 dataset has a varying number of annotators for each comment whereas for the 2022 dataset, each group has 5 annotators. Therefore we consider random 5 annotators for each comment from the 2017 dataset.
We transform the 2023 dataset (expert annotators) dataset and the 2022 dataset to binary scale since the 2017 dataset is in binary scale in order to maintain consistency and quantitatively analyze all three datasets in an effective manner.

§ SIMULATED TOXIC DATASET EXPERIMENT DETAILS

§.§ Simulated dataset

To simulate a dataset, we first create an entire matrix of ratings from $N$ annotators and $M$ items, resulting in a fully rated dataset. We then reduce the replication to more realistic levels by randomly sampling down ratings per item. If the replication is $P$ that refers to $M$ items annotated by $P$ annotators.
Then we establish a set of attributes(e.g. annotator skill level and comment difficulty level). For annotators' skill level, there are 4 attributes : (1) Expert (2) Average (3) Bad (4) Random whereas in the case of comment difficulty level, there are 3 attributes : (1) Easy (2) Normal (3) Hard. They are described as follows:

* Annotator skill level:

* Expert: Annotators whose annotation is 90% accurate with respect to the ground truth

* Average: Annotators whose annotation is 75% accurate with respect to the ground truth

* Bad: Annotators whose annotation is 25% accurate with respect to ground truth

* Random: Annotators who annotate 50% of the comments as toxic or not toxic uniformly at random

* Comment difficulty level:

* Easy: Annotators perform at 120% of their skill

* Normal: Annotators perform as expected

* Hard: Annotators perform at 70% of their skill

We randomly initialize attributes to each annotator and item and use these attributes to probabilistically assign an annotation for each annotator and item interaction. We then create items and annotator embeddings using the following way
We first created a sparse binary matrix of item-annotator items. Then we used WALS factorization [27] to do the dimensionality reduction to create items and annotator embeddings.
Then we use HBDSCAN for clustering that.

§.§ Ratio Details

(1) Ground truth ratio is 1:1 for toxic: not toxic
(2) Annotator type ratio is 1:1:1:5 for expert:random:bad:average.
(3) Item type ratio is 1:2:1 for easy:normal:hard.

§.§ Item-Annotator Embedding

We first created a sparse binary matrix of item-annotator items. Then we used WALS factorization [27] to do the dimensionality reduction to create items and annotator embeddings.

This formalizes the idea that two annotators are likely to be more similar if they both annotate the same item similarly. And analogously, two items are likely to be more similar if an annotator annotates the same item similarly.

§.§ Annotators and Item embedding Error in experiment

Positive Train: 0.71, Negative Train: 0.72, Positive Dev: 0.75, Negative Dev: 0.76.

§.§ Different options in the experiment to improve results
with lower replication

* Scale/normalize/whiten the embedding before running UMAP: Scaling and normalizing correspond to subtracting the mean and normalizing to unit variance, while whitening means removing correlations among features.

* Reduced the dev/train error ratio: In the original run, we used 3 dimensions, reg 0.1, and 5 iterations. We tried to perturb dimensions, reg, and iterations to see the scope of improvement. We looked in the range dim =[2,3,5], reg = [0.01, 0.1,1, 10] and iterations =[2,5,8] and saw that dimension 3, reg=10 and iteration = 5 work better which results in the following train/dev error: Positive Train: 0.68, Negative Train: 0.69, Positive Dev: 0.80, and Negative Dev: 0.81.

* Trying different UMAP parameters: We modified the UMAP parameter of neighbors, which was set at 15 by default. We selected 30 because lower numbers result in a more dense chain-like structure, whereas higher values (e.g., neighbors = 30) result in a less dense chain-like structure, which helps the clustering method to perform better.

§.§ Effect of majority vote

Figure <ref> shows When a majority vote is used as ground truth then the AUC decreases as the replication decreases.

Effects of majority vote as a proxy ground truth by replication size

§ LLM EXPERIMENT DETAILS

§.§.§ Model Training

We train our LLM using soft prompt tuning [29]. This allows us to efficiently tune the LLM on 10 - 500 examples. The LLM we use is the 62b FLAN-cont-PaLM [38]. We tuned a prompt consisting of 100 tokens, each with an embedding of dimension 8192 for the 62B model. We trained these tokens with a basic Adam optimizer with clipped gradients. We chose a default learning rate and schedule for all our experiments and did not tune hyperparameters further. We set the sampling temperature to 0 and fixed the prompt initialization, to reduce randomness. For each training run, we have a train, validation, and test set with balanced positive and negative classes. We train for 20-40 epochs and use a validation set to pick the final model for each run. We follow the setup described in [39] for the construction of the few shots and soft tuning. We used sklearn for accuracy metrics, whereas TensorFlow internals were used for the model training and implementation.


# FOKE: A Personalized and Explainable Education Framework Integrating
Foundation Models, Knowledge Graphs, and Prompt Engineering

SILAN HU
Macau University of Science and Technology
Macau, China
<EMAIL_ADDRESS>
&Xiaoning Wang
Communication University of China
Beijing, China
<EMAIL_ADDRESS>

###### Abstract

Integrating large language models (LLMs) and knowledge graphs (KGs) holds
great promise for revolutionizing intelligent education, but challenges remain
in achieving personalization, interactivity, and explainability. We propose
FOKE, a Forest Of Knowledge and Education framework that synergizes foundation
models, knowledge graphs, and prompt engineering to address these challenges.
FOKE introduces three key innovations: (1) a hierarchical knowledge forest for
structured domain knowledge representation; (2) a multi-dimensional user
profiling mechanism for comprehensive learner modeling; and (3) an interactive
prompt engineering scheme for generating precise and tailored learning
guidance.

We showcase FOKE’s application in programming education, homework assessment,
and learning path planning, demonstrating its effectiveness and practicality.
Additionally, we implement Scholar Hero, a real-world instantiation of FOKE.
Our research highlights the potential of integrating foundation models,
knowledge graphs, and prompt engineering to revolutionize intelligent
education practices, ultimately benefiting learners worldwide. FOKE provides a
principled and unified approach to harnessing cutting-edge AI technologies for
personalized, interactive, and explainable educational services, paving the
way for further research and development in this critical direction.

Keywords: Intelligent education, Large Language Models, Prompt Engineering,
Personalization, Knowledge Representation

##  1 Introduction

###  1.1 Background

The rapid development of Artificial Intelligence (AI) has profoundly impacted
various domains, and education is no exception. AI-empowered education has the
potential to revolutionize traditional teaching and learning practices, making
high-quality education more accessible and affordable to learners from diverse
backgrounds. By leveraging advanced AI technologies, such as Large Language
Models (LLMs), intelligent tutoring systems, and adaptive learning platforms,
AI-powered educational tools can provide personalized, interactive, and
engaging learning experiences, catering to the individual needs and
preferences of each learner [1][2].

Among the various AI technologies, LLMs have emerged as a game-changer in the
educational landscape. LLMs, such as GPT-4 [3] and PaLM 2[4], possess
remarkable capabilities in natural language understanding, generation, and
reasoning, enabling a wide range of applications in education, including
automatic question answering, essay scoring, and knowledge recommendation [5].
For instance, the integration of ChatGPT, a state-of-the-art conversational AI
model, into language learning environments has shown promising results in
enhancing learners’ motivation, engagement, and learning outcomes [6].

The potential of LLMs to democratize access to high-quality educational
resources and support has attracted increasing attention from researchers and
practitioners. [7] explored the opportunities and challenges of integrating
LLMs into data science education, highlighting their roles in generating
personalized learning content, providing adaptive feedback, and fostering
interactive learning experiences. By harnessing the power of LLMs, educational
institutions and platforms can offer affordable, scalable, and effective
educational services to learners who may not have access to traditional high-
quality educational resources, such as expert teachers or well-equipped
classrooms.

However, the application of LLMs in educational scenarios is still in its
early stages and faces several challenges. First, educational contexts often
involve complex domain knowledge and diverse learner backgrounds, requiring
LLMs to provide personalized and adaptive learning support. Second, effective
education relies on interactive and engaging learning experiences, which
demands LLMs to maintain coherent and meaningful dialogue with learners.
Third, educational applications of LLMs should offer interpretable and
trustworthy explanations to facilitate learners’ understanding and teachers’
intervention [8].

To address these challenges and fully unleash the potential of LLMs in
democratizing high-quality education, we propose FOKE (Forest Of Knowledge and
Education), a novel framework that integrates foundation models, knowledge
graphs, and prompt engineering. FOKE aims to provide personalized,
interactive, and explainable educational services by leveraging the strengths
of these advanced techniques. In the following sections, we will introduce the
key components and methodologies of FOKE in detail.

###  1.2 Motivation

Prompt engineering, an emerging paradigm in LLM application, has shown
promising potential to address the aforementioned challenges. Prompt
engineering refers to the process of designing and optimizing prompt
templates, which are natural language instructions that guide LLMs to perform
specific tasks [9]. By carefully crafting prompts, researchers have
successfully adapted LLMs to a variety of downstream applications, such as
visual question answering [10], text classification [11], and code
augmentation [12]. The success of prompt engineering can be attributed to its
ability to leverage the knowledge and skills embedded in LLMs while providing
a flexible and controllable interface for task customization.

In the context of intelligent education, prompt engineering holds great
promise for enabling personalized, interactive, and explainable LLM-based
services. By incorporating learner-specific information and domain knowledge
into prompts, LLMs can generate tailored learning content and provide adaptive
feedback to individual students. By designing conversation-oriented prompts,
LLMs can engage in meaningful dialogue with learners and offer timely support
and guidance. By injecting rationales and explanations into prompts, LLMs can
produce interpretable and trustworthy educational suggestions and assessments.
Therefore, the integration of prompt engineering and LLMs opens up new
opportunities for revolutionizing intelligent education practices.

###  1.3 Research Objectives

Motivated by the potential of prompt engineering and LLMs in intelligent
education, we propose FOKE, a novel framework that aims to achieve the
following objectives:

  * •

Objective 1: Develop knowledge representation and user modeling techniques for
personalized learning. This objective includes (1) designing a hierarchical
knowledge representation scheme to encode domain knowledge and support
personalized learning paths; and (2) developing a multi-dimensional user
profiling mechanism to capture learner characteristics, preferences, and
styles, enabling adaptive educational services.

  * •

Objective 2: Explore prompt engineering approaches for generating precise and
interpretable learning guidance. Under this objective, we aim to (1) design a
structured prompt representation approach to elicit accurate and coherent
feedback from LLMs; and (2) investigate the integration of knowledge
representation and user modeling to facilitate personalized recommendation and
adaptive instruction.

  * •

Objective 3: Showcase the application scenarios and potential impact of FOKE
in educational contexts. This objective involves (1) demonstrating the
effectiveness and advantages of FOKE through case studies in programming
education, homework assessment, learning path planning, etc.; and (2)
discussing the potential of FOKE in enhancing intelligent education practices
and improving learning outcomes.

By achieving these objectives, FOKE is expected to provide a powerful and
flexible framework for designing and deploying LLM-based intelligent education
applications, while addressing the key challenges of personalization,
interactivity, and interpretability.

###  1.4 Contributions

The main contributions of this work are summarized as follows:

  1. 1.

We propose FOKE, a novel framework that synergizes prompt engineering and LLMs
to enable personalized, interactive, and explainable intelligent education
services. Central to FOKE is a hierarchical knowledge forest, which represents
domain knowledge as a tuple $\mathcal{KF}=(\mathcal{C},\mathcal{R})$, where
$\mathcal{C}$ denotes the set of concept nodes and $\mathcal{R}$ represents
the set of relation edges.

  2. 2.

We design a multi-dimensional user profiling mechanism that integrates
structured attributes, unstructured behaviors, and temporal trajectories to
comprehensively model learner characteristics, which can be formulated as
$\mathcal{UP}=(\mathcal{A},\mathcal{B},\mathcal{T})$, where $\mathcal{A}$,
$\mathcal{B}$, and $\mathcal{T}$ correspond to the attribute, behavior, and
trajectory dimensions, respectively. Based on the user profile, we propose a
structured prompt representation scheme that incorporates goal specification,
explanatory information, and feedback indicators to generate precise and
informative learning guidance, with the prompt template represented as
$\mathcal{PT}=(\mathcal{G},\mathcal{E},\mathcal{F})$, where $\mathcal{G}$,
$\mathcal{E}$, and $\mathcal{F}$ denote the goal, explanation, and feedback
components, respectively.

  3. 3.

We develop a graph embedding-based approach to fuse knowledge representation
and user modeling, which learns low-dimensional vector representations for
concepts and learners. The embedding learning process can be formulated as an
optimization problem:

$\min_{\mathbf{h}}\sum_{(c_{i},c_{j},r)\in\mathcal{D}}\mathcal{L}(\mathbf{h}_{c_{i}},\mathbf{h}_{c_{j}},\mathbf{h}_{r}),$
(1)

where $\mathbf{h}_{c_{i}}$, $\mathbf{h}_{c_{j}}$, and $\mathbf{h}_{r}$ are the
embeddings of concepts $c_{i}$, $c_{j}$, and relation $r$, respectively, and
$\mathcal{D}$ is the set of concept-relation triples. We designed three
educational tasks to showcase the application of the FOKE framework:
interactive programming education, personalized learning path planning, and
intelligent homework assessment. Although a comprehensive evaluation of the
framework is yet to be completed, AI educational products based on FOKE have
already begun experiments and optimization in actual classroom teaching. By
collecting feedback from students and teachers, we are continuously improving
the system’s performance and user experience. However, it is worth noting that
currently in the field of intelligent education, there is a lack of
standardized evaluation methods to compare different products, frameworks, or
services, mainly due to the diversity of intelligent education systems, domain
specificity, and differences in user needs. Establishing a comprehensive,
fair, and effective set of evaluation criteria and methods is crucial for
advancing the development of the intelligent education field. In our future
work, we plan to collaborate with domain experts to provide more experience
and suggestions for the evaluation of intelligent education systems while
continuing to focus on improving and applying the FOKE framework, striving to
achieve greater breakthroughs in personalization, interactivity, and
interpretability, and contribute more innovative solutions to the field of
intelligent education.

###  1.5 Organization

The remainder of this paper is organized as follows. Section 2 reviews the
related work on intelligent tutoring systems, knowledge representation, and
personalized learning. Section 3 presents the proposed FOKE framework in
detail, including the integration of foundation models and knowledge graphs,
the construction of knowledge forests, the profiling of users, the engineering
of interactive prompts, and the generation of explainable recommendations.
Section 4 describes three representative application scenarios of FOKE, i.e.,
programming education, writing assessment, and learning path planning, and
showcases the workflow and examples in each scenario. Section 5 introduces the
Scholar Hero system, an instantiation of FOKE in real-world educational
settings, and reports its deployment, evaluation, and impact. Section 6
concludes the paper and discusses future research directions.

##  2 Related Work

In this section, we review the related work on AI in education, large language
models, and prompt engineering, which form the theoretical and technical
foundations of our proposed FOKE framework.

###  2.1 AI in Education

The application of AI in education has a long history, dating back to the
1970s when intelligent tutoring systems (ITS) were first introduced [13].
Since then, AI techniques have been extensively explored to enhance various
aspects of education, such as personalized learning [14], adaptive assessment
[15], and learning analytics [16]. For example, Bayesian knowledge tracing
[17] was proposed to model student knowledge acquisition and predict their
performance. Reinforcement learning [18] was employed to optimize
instructional strategies and improve learning outcomes. Deep learning [19] was
applied to capture complex learning patterns and enable intelligent tutoring
interventions.

Despite the significant progress, existing AI-enhanced educational systems
still face limitations in terms of adaptability, interactivity, and
interpretability. Most systems rely on pre-defined rules or models, which may
not effectively capture the diversity and dynamics of real-world educational
scenarios. Moreover, the interaction between AI systems and learners is often
restricted to simplistic forms, such as multiple-choice questions or short-
answer responses, lacking the flexibility and naturalness of human-like
communication. Furthermore, many AI models are black boxes, providing little
explanatory information to help learners understand and trust the generated
educational feedback. These limitations highlight the need for more advanced
AI techniques that can enable personalized, interactive, and explainable
education.

###  2.2 Large Language Models

The emergence of large language models (LLMs) has revolutionized the field of
natural language processing (NLP) and opened up new opportunities for AI
applications in various domains. LLMs, such as GPT-4 [3], BERT [20], are deep
neural networks pre-trained on massive text corpora, which can learn rich
linguistic knowledge and perform a wide range of language tasks. For example,
GPT-4 has shown impressive capabilities in language generation, translation,
summarization, and question answering, surpassing human performance in some
cases [3]. BERT has achieved state-of-the-art results on various NLP
benchmarks, such as GLUE [21] and SQuAD [22], demonstrating its effectiveness
in language understanding and reasoning.

The success of LLMs can be attributed to their ability to capture the
intricate patterns and semantics of natural language from large-scale
unsupervised learning. By training on diverse text data, LLMs can acquire a
broad range of knowledge and skills, which can be transferred to downstream
tasks with minimal fine-tuning. This transfer learning paradigm has greatly
reduced the need for task-specific labeled data and enabled the development of
generalizable and scalable AI systems. Moreover, the pre-training objectives
of LLMs, such as masked language modeling [23] and permutation language
modeling [24], have been designed to encourage the models to learn contextual
representations and long-range dependencies, which are essential for
understanding and generating coherent and informative text.

However, the application of LLMs in education is still an emerging area that
requires further exploration. Existing work has mainly focused on using LLMs
for automated essay scoring [25], question answering [26]. For example, Xue.J
et al. [27] fine-tuned BERT on a large dataset of student essays and their
corresponding scores, and achieved state-of-the-art performance on automated
essay scoring. Abdelghani et al. [28] employed GPT-3 to generate diverse and
informative answers to student questions, and demonstrated its potential for
intelligent tutoring systems. While these studies have shown promising
results, they have not fully exploited the potential of LLMs for interactive
and explainable education, which is the focus of our FOKE framework.

###  2.3 Prompt Engineering

Prompt engineering is an emerging technique that aims to elicit desired
behaviors from LLMs by designing and optimizing natural language prompts.
Unlike traditional fine-tuning approaches that adapt LLMs to specific tasks
through end-to-end training, prompt engineering keeps the LLM parameters fixed
and instead crafts input prompts that can guide the model to perform the
target task. This paradigm has several advantages, such as reducing the need
for task-specific training data, enabling zero-shot or few-shot learning, and
preserving the general knowledge and capabilities of LLMs.

The key idea of prompt engineering is to frame the task objective as a natural
language prompt that can be easily understood and followed by LLMs. For
example, to perform sentiment classification, a prompt template can be
designed as "Classify the sentiment of the following text: [Text]. The
sentiment is [Sentiment].", where [Text] is the input text and [Sentiment] is
the target sentiment label. By filling in the template with specific text
instances and feeding them to LLMs, the models can generate the corresponding
sentiment labels based on their language understanding and reasoning
abilities.

In the context of education, prompt engineering has the potential to enable
personalized, interactive, and explainable LLM-based services. By
incorporating learner-specific information and domain knowledge into prompts,
LLMs can generate adaptive learning content and provide customized feedback.
By designing conversation-oriented prompts, LLMs can engage in meaningful
dialogue with learners and offer timely support and guidance. By injecting
explanations and justifications into prompts, LLMs can produce interpretable
and trustworthy educational suggestions and assessments. However, existing
work on prompt engineering in education is still limited, which motivates us
to develop the FOKE framework to fill this gap.

##  3 FOKE Framework

In this section, we present the proposed FOKE framework in detail. We first
provide an overview of the framework architecture and then elaborate on its
key components, including the knowledge forest, user profiling, prompt
representation, and graph embedding modules.

Figure 1: Overview of the FOKE Framework

###  3.1 Overview

The framework takes as input a domain knowledge base, a user profile database,
and a learning task specification, and generates personalized, interactive,
and explainable learning guidance and feedback through the integration of
knowledge representation, user modeling, prompt engineering, and graph
embedding techniques.

Specifically, the domain knowledge base is first processed by the knowledge
forest module to construct a hierarchical and structured representation of the
key concepts and their relationships. The user profile database is then
encoded by the user profiling module to capture the learners’ attributes,
behaviors, and temporal trajectories in a multi-dimensional manner. The
learning task specification, along with the extracted knowledge and user
representations, is fed into the prompt representation module to generate a
set of structured prompts that guide the LLM to produce tailored and
informative learning support. Finally, the graph embedding module fuses the
knowledge and user representations through graph embedding techniques, which
enables personalized recommendation and adaptive instruction based on the
similarity and relevance of concepts and learners in the embedding space.

In the following subsections, we present each module of FOKE in more detail
and formulate their key components and algorithms.

###  3.2 Knowledge Forest

The knowledge forest module introduces a novel and flexible approach to
representing and organizing domain knowledge, addressing the limitations of
traditional knowledge graphs. Unlike knowledge graphs, which typically focus
on a single, static representation of knowledge, the knowledge forest allows
for the dynamic integration of multiple knowledge trees, providing a more
adaptable and efficient framework for knowledge acquisition and reasoning.

Formally, we define a knowledge forest as a collection of knowledge trees
$\mathcal{KF}=\\{\mathcal{T}_{1},\mathcal{T}_{2},\dots,\mathcal{T}_{K}\\}$,
where each tree
$\mathcal{T}_{k}=(\mathcal{C}_{k},\mathcal{R}_{k},\mathcal{A}_{k})$ represents
a hierarchical structure of concepts $\mathcal{C}_{k}$, relations
$\mathcal{R}_{k}$, and attributes $\mathcal{A}_{k}$. The key idea behind the
knowledge forest is to enable the dynamic insertion and removal of knowledge
trees based on the specific learning context and requirements, allowing for a
more flexible and efficient knowledge representation compared to traditional
knowledge graphs [29].

The construction of the knowledge forest follows a two-stage approach, similar
to the single-tree case. In the first stage, we extract concepts and their
attributes from the domain knowledge base using natural language processing
techniques, such as named entity recognition [30], relation extraction [31],
and semantic role labeling [32]. However, instead of constructing a single
global concept set, we allow for the creation of multiple concept sets
$\\{\mathcal{C}_{k}\\}_{k=1}^{K}$, each corresponding to a specific knowledge
tree. This enables the knowledge forest to capture different aspects or
subdomains of the knowledge base, providing a more fine-grained and context-
specific representation.

In the second stage, we infer the semantic relationships within each knowledge
tree $\mathcal{T}_{k}$ using graph mining and learning algorithms. However,
the key difference lies in the time complexity of the inference process. In
traditional knowledge graphs, the relation inference typically involves a
global search over the entire concept space, resulting in a high time
complexity of $O(|\mathcal{C}|^{2})$, where $|\mathcal{C}|$ is the total
number of concepts. In contrast, the knowledge forest allows for a local
search within each knowledge tree, reducing the time complexity to
$O(\sum_{k=1}^{K}|\mathcal{C}_{k}|^{2})$, where $|\mathcal{C}_{k}|$ is the
number of concepts in the $k$-th tree [33]. Since $|\mathcal{C}_{k}|$ is
typically much smaller than $|\mathcal{C}|$, the knowledge forest enables a
more efficient relation inference process.

Moreover, the knowledge forest introduces a novel tree-level inference
mechanism to capture the relationships between different knowledge trees. Let
$\mathbf{h}_{k}^{(L)}$ be the root embedding of the $k$-th knowledge tree,
obtained by aggregating the embeddings of its concepts using a pooling
function, such as mean or max pooling. We can then infer the tree-level
relationships by measuring the similarity between the root embeddings:

$r(T_{i},T_{j})=\begin{cases}1,&\text{if
}\mathrm{sim}(\mathbf{h}_{i}^{(L)},\mathbf{h}_{j}^{(L)})\geq\tau,\\\
0,&\text{otherwise},\end{cases}$ (2)

where $\mathrm{sim}(\cdot)$ is a similarity function, and $\tau$ is a
predefined threshold. This tree-level inference allows the knowledge forest to
capture the high-level relationships between different subdomains or aspects
of knowledge, providing a more comprehensive and structured representation of
the domain.

The dynamic nature of the knowledge forest enables efficient updates and
adaptations to new knowledge. When a new knowledge tree $\mathcal{T}_{K+1}$ is
discovered or constructed, it can be seamlessly inserted into the knowledge
forest without the need to reconstruct the entire graph. Similarly, when a
knowledge tree becomes obsolete or irrelevant, it can be easily removed from
the forest. This dynamic property allows the knowledge forest to evolve and
grow with the expanding knowledge base, making it a more scalable and
maintainable solution compared to traditional knowledge graphs.

The knowledge forest also enables more efficient and targeted knowledge
retrieval and reasoning. Given a query concept $c_{q}$, we can first identify
the most relevant knowledge tree $\mathcal{T}_{k^{*}}$ based on the tree-level
similarity:

$k^{*}=\underset{k\in\\{1,2,\dots,K\\}}{\operatorname{argmax}}\,\mathrm{sim}(\mathbf{h}_{q},\mathbf{h}_{k}^{(L)}),$
(3)

where $\mathbf{h}_{q}$ is the embedding of the query concept, and
$\mathbf{h}_{k}^{(L)}$ is the root embedding of the $k$-th knowledge tree.
Once the relevant tree is identified, we can perform a local search within
$\mathcal{T}_{k^{*}}$ to retrieve the most relevant concepts and relations,
avoiding the need to search the entire knowledge graph [29]. This targeted
retrieval process significantly reduces the search space and improves the
efficiency of knowledge reasoning.

Furthermore, the knowledge forest enables personalized learning and
recommendation by leveraging the hierarchical structure and tree-level
relationships. By maintaining a student’s knowledge state
$\mathbf{s}=(s_{1},s_{2},\dots,s_{K})$, where $s_{k}\in[0,1]$ represents the
student’s mastery level of the $k$-th knowledge tree, we can recommend the
most suitable knowledge tree for the student to explore next:

$k_{\text{next}}=\underset{k\in\\{1,2,\dots,K\\}}{\operatorname{argmax}}\left(\sum_{i=1}^{K}r(T_{i},T_{k})\cdot
s_{i}\right)\cdot(1-s_{k})$ (4)

where the first term measures the relevance of the $k$-th knowledge tree to
the student’s current knowledge state, and the second term ensures that the
recommended tree is not yet fully explored by the student. This personalized
recommendation strategy helps students navigate the knowledge forest
efficiently and effectively, enhancing their learning experience and outcomes.

The knowledge forest also facilitates the integration of multi-modal learning
resources, such as text, images, and videos, by associating them with the
relevant concepts and relations in the knowledge trees [34]. This multi-modal
representation enhances the richness and diversity of the learning content,
catering to different learning styles and preferences.

Moreover, the knowledge forest supports explainable reasoning and decision-
making by providing a transparent and interpretable representation of the
knowledge structure. The hierarchical organization of concepts and the
explicit modeling of semantic relationships enable the generation of human-
understandable explanations for the recommended learning paths and resources.

In summary, the knowledge forest module introduces a dynamic, flexible, and
efficient approach to representing and organizing domain knowledge. By
allowing for the integration of multiple knowledge trees and enabling tree-
level inference, personalized recommendation, multi-modal learning, and
explainable reasoning, the knowledge forest addresses the limitations of
traditional knowledge graphs and provides a more scalable and adaptable
solution for knowledge acquisition and reasoning in educational applications.

###  3.3 User Profiling

The user profiling module aims to capture the learners’ characteristics and
preferences in a multi-dimensional manner to enable personalized and adaptive
learning support. We define a user profile as a tuple
$\mathcal{UP}=(\mathcal{A},\mathcal{B},\mathcal{T})$, where $\mathcal{A}$,
$\mathcal{B}$, and $\mathcal{T}$ denote the attribute, behavior, and
trajectory dimensions, respectively.

The attribute dimension $\mathcal{A}$ represents the learner’s static and
demographic information, such as age, gender, education level, and learning
style. The behavior dimension $\mathcal{B}$ captures the learner’s dynamic and
interactive patterns, such as learning activities, quiz performance, and forum
participation. The trajectory dimension $\mathcal{T}$ models the learner’s
temporal and sequential characteristics, such as learning paths, knowledge
states, and skill acquisition.

To construct the user profiles, we propose a multi-view representation
learning approach. For each dimension, we extract the relevant features from
the user profile database and learn a low-dimensional vector representation
using techniques such as matrix factorization [35], deep learning [36], and
sequential modeling [37]. We then fuse the learned representations from
different dimensions using attention mechanisms [38] or graph convolutional
networks [39] to obtain a unified user embedding.

Formally, let $\mathbf{a}_{u}$, $\mathbf{b}_{u}$, and $\mathbf{t}_{u}$ denote
the attribute, behavior, and trajectory embeddings of user $u$, respectively.
The unified user embedding $\mathbf{h}_{u}$ can be obtained as:

$\mathbf{h}_{u}=f(\mathbf{a}_{u},\mathbf{b}_{u},\mathbf{t}_{u};\Theta),$ (5)

where $f$ is the fusion function with parameters $\Theta$. For example, an
attention-based fusion function can be defined as:

$\displaystyle\alpha_{a},\alpha_{b},\alpha_{t}$
$\displaystyle=\text{softmax}(\mathbf{w}_{a}^{\top}\mathbf{a}_{u},\mathbf{w}_{b}^{\top}\mathbf{b}_{u},\mathbf{w}_{t}^{\top}\mathbf{t}_{u}),$
(6) $\displaystyle\mathbf{h}_{u}$
$\displaystyle=\alpha_{a}\mathbf{a}_{u}+\alpha_{b}\mathbf{b}_{u}+\alpha_{t}\mathbf{t}_{u},$
(7)

where $\mathbf{w}_{a}$, $\mathbf{w}_{b}$, and $\mathbf{w}_{t}$ are the
attention weight vectors for the attribute, behavior, and trajectory
dimensions, respectively.

###  3.4 Prompt Representation

The prompt representation module aims to generate structured and informative
prompts that can guide the LLM to produce personalized and explainable
learning support. We define a prompt as a tuple
$\mathcal{PT}=(\mathcal{G},\mathcal{E},\mathcal{F})$, where $\mathcal{G}$,
$\mathcal{E}$, and $\mathcal{F}$ denote the goal, explanation, and feedback
components, respectively.

The goal component $\mathcal{G}$ specifies the learning objective and task
requirements, such as knowledge acquisition, problem-solving, or skill
practice. The explanation component $\mathcal{E}$ provides the relevant
background information and reasoning process to help learners understand the
key concepts and their relationships. The feedback component $\mathcal{F}$
offers personalized suggestions and assessments based on the learner’s
performance and characteristics.

To generate the structured prompts, we propose a template-based approach that
leverages the knowledge forest and user profile representations. Specifically,
for each learning task, we first retrieve the relevant concepts and relations
from the knowledge forest based on the task specification. We then instantiate
the prompt template with the retrieved knowledge and the learner’s attributes
and preferences. Finally, we optimize the prompt template and instantiation
using techniques such as reinforcement learning [40] to improve the quality
and diversity of the generated prompts.

Formally, let $\mathcal{KF}_{t}\subseteq\mathcal{KF}$ denote the subset of the
knowledge forest relevant to task $t$, and $\mathbf{h}_{u}$ denote the user
embedding of learner $u$. The prompt generation process can be formulated as:

$\displaystyle\mathcal{G}_{t}$
$\displaystyle=g(\mathcal{KF}_{t},\mathbf{h}_{u};\Theta_{g}),$ (8)
$\displaystyle\mathcal{E}_{t}$
$\displaystyle=e(\mathcal{KF}_{t},\mathbf{h}_{u};\Theta_{e}),$ (9)
$\displaystyle\mathcal{F}_{t}$
$\displaystyle=f(\mathcal{KF}_{t},\mathbf{h}_{u};\Theta_{f}),$ (10)
$\displaystyle\mathcal{PT}_{t}$
$\displaystyle=(\mathcal{G}_{t},\mathcal{E}_{t},\mathcal{F}_{t}),$ (11)

where $g$, $e$, and $f$ are the generation functions for the goal,
explanation, and feedback components, respectively, with parameters
$\Theta_{g}$, $\Theta_{e}$, and $\Theta_{f}$.

For example, a template for the goal component can be defined as:

> Acquire knowledge about [Concept] and its relations to [RelatedConcepts] to
> solve problems of type [ProblemType].

where [Concept], [RelatedConcepts], and [ProblemType] are slots to be filled
with the retrieved knowledge and user information.

The template can be instantiated as:

> Acquire knowledge about dynamic programming and its relations to recursion
> and memoization to solve problems of type optimization, given your interest
> in algorithms and competitive programming.

The quality of the generated prompt $\mathcal{PT}t$ can be evaluated based on
metrics such as relevance, coherence, and diversity, and optimized using
reinforcement learning objectives such as:

$\max{\Theta_{g},\Theta_{e},\Theta_{f}}\mathbb{E}_{\mathcal{PT}_{t}\sim
p(\cdot|\mathcal{KF}_{t},\mathbf{h}_{u})}[R(\mathcal{PT}_{t})],$ (12)

where $R(\mathcal{PT}_{t})$ is the reward function that measures the quality
and effectiveness of the prompt.

###  3.5 Graph Embedding

The graph embedding module aims to learn a unified representation of the
knowledge forest and user profiles to enable personalized recommendation and
adaptive instruction. We propose a graph embedding approach that learns low-
dimensional vector representations of concepts and users based on their
structural and semantic relationships.

Formally, let $\mathcal{G}=(\mathcal{V},\mathcal{E})$ denote the graph
constructed from the knowledge forest $\mathcal{KF}$ and user profiles
${\mathcal{UP}_{u}}$, where $\mathcal{V}=\mathcal{C}\cup\mathcal{U}$ is the
set of nodes (concepts and users) and $\mathcal{E}$ is the set of edges
(relations and interactions). The goal is to learn a function
$f:\mathcal{V}\rightarrow\mathbb{R}^{d}$ that maps each node to a
$d$-dimensional vector, such that the graph structure and semantics are
preserved in the embedding space.

We adopt a graph neural network (GNN) approach to learn the node embeddings.
At each layer $l$, the embedding of node $v$ is updated as:

$\displaystyle\mathbf{m}_{v}^{(l)}$
$\displaystyle=\text{AGG}({\mathbf{h}_{u}^{(l-1)}:u\in\mathcal{N}(v)}),\
\mathbf{h}_{v}^{(l)}$
$\displaystyle=\text{UPDATE}(\mathbf{h}_{v}^{(l-1)},\mathbf{m}_{v}^{(l)}),$
(13)

where $\mathbf{h}_{v}^{(l)}$ is the embedding of node $v$ at layer $l$,
$\mathcal{N}(v)$ is the set of neighbors of $v$, and AGG and UPDATE are the
aggregation and update functions, respectively.

The GNN is trained using a combination of supervised and unsupervised
objectives. For supervised learning, we use the node embeddings to predict the
node labels (e.g., concept categories or user clusters) based on a cross-
entropy loss:

$\mathcal{L}s=-\sum{v\in\mathcal{V}L}\sum{c=1}^{C}y_{vc}\log\hat{y}{vc},$ (14)

where $\mathcal{V}L\subseteq\mathcal{V}$ is the set of labeled nodes, $C$ is
the number of classes, $y{vc}$ is the true label of node $v$ for class $c$,
and $\hat{y}{vc}$ is the predicted probability.

For unsupervised learning, we use a contrastive loss [41] to enforce the
similarity of embeddings for neighboring nodes and the dissimilarity of
embeddings for random pairs of nodes:

$\mathcal{L}u=-\sum{(u,v)\in\mathcal{E}}\log\frac{\exp(\text{sim}(\mathbf{h}_{u},\mathbf{h}v))}{\sum{v^{\prime}\in\mathcal{V}}\exp(\text{sim}(\mathbf{h}u,\mathbf{h}{v^{\prime}}))},$
(15)

where $\text{sim}(\cdot,\cdot)$ is a similarity function such as cosine
similarity or dot product.

The overall training objective is a weighted combination of the supervised and
unsupervised losses:

$\mathcal{L}=\lambda_{s}\mathcal{L}_{s}+\lambda_{u}\mathcal{L}_{u},$ (16)

where $\lambda_{s}$ and $\lambda_{u}$ are the weighting coefficients.

After training, the learned node embeddings can be used for various downstream
tasks, such as concept recommendation, prerequisite prediction, and learning
path generation, based on the similarity and relevance of the embeddings.

##  4 Application Scenarios

To demonstrate the practical value and wide applicability of the proposed FOKE
framework, we showcase its potential in three representative educational
scenarios: interactive programming education, personalized learning path
planning, and intelligent writing assessment. In this section, we describe the
workflow and key functionalities of FOKE in each scenario, and provide
illustrative examples to show its effectiveness in generating personalized and
explainable learning support.

Figure 2: Scholar Hero: The Application of the FOKE framework.

###  4.1 Interactive Programming Education

Programming education is a challenging yet important domain that requires
learners to acquire complex concepts and skills. FOKE can be applied to
provide interactive and adaptive programming tutorials and feedback to enhance
the learning experience and outcome.

The workflow of FOKE in programming education is as follows:

  1. 1.

The knowledge forest of programming concepts and skills is constructed from
various learning resources such as textbooks, online courses, and coding
platforms.

  2. 2.

The user profile of each learner is built based on their background,
performance, and learning activities in programming.

  3. 3.

For each programming task or project, FOKE generates a set of personalized
prompts that guide the learner through the problem-solving process, such as
understanding the requirements, designing the solution, and debugging the
code.

  4. 4.

The learner interacts with the prompts and receives adaptive feedback and
suggestions based on their input and progress.

  5. 5.

The knowledge forest and user profile are updated based on the learner’s
performance and feedback to enable continuous optimization and
personalization.

For example, suppose a learner is working on a Python project to implement a
recommendation system. FOKE may generate the following prompts to guide the
learner:

> Prompt 1: Let’s break down the project requirements into smaller tasks.
> Based on your experience with Python and machine learning, what are the key
> steps you would take to build a recommendation system?
> Prompt 2: Great insights! To implement the collaborative filtering
> algorithm, you need to first construct the user-item interaction matrix.
> Here are a few examples of how to represent the matrix using NumPy or
> Pandas. Which representation do you think is more suitable for your project
> and why?
> Prompt 3: Your code looks good so far. I noticed that you used a nested loop
> to calculate the user similarity scores, which may be inefficient for large
> datasets. Have you considered using matrix operations or libraries like
> SciPy to speed up the computation? Here are a few suggestions…

Through the interactive and explainable prompts, FOKE can help learners deepen
their understanding of programming concepts, improve their problem-solving
skills, and receive timely and actionable feedback. The personalization and
adaptivity of the prompts also cater to the diverse needs and preferences of
learners, making the learning process more engaging and effective.

###  4.2 Personalized Learning Path Planning

Learning path planning is a critical task in education that aims to recommend
a sequence of courses or learning activities for learners to achieve their
learning goals. FOKE can be applied to generate personalized learning paths
based on the learner’s knowledge state, learning style, and career aspiration.

The application mechanism of FOKE in learning path planning is as follows:

  1. 1.

The knowledge forest of course prerequisites, topics, and learning outcomes is
constructed from the course catalog and syllabus.

  2. 2.

The user profile of each learner is built based on their academic record,
learning preferences, and career goals.

  3. 3.

FOKE generates a set of candidate learning paths based on the knowledge forest
and the learner’s profile, using techniques such as prerequisite-based
filtering, collaborative filtering, and goal-based reasoning.

  4. 4.

The candidate learning paths are ranked and recommended to the learner based
on their predicted performance, satisfaction, and alignment with their goals.

  5. 5.

The learner explores and selects the learning paths, and provides feedback and
preference to update their profile and refine the recommendation.

For instance, consider a college student majoring in computer science who
wants to become a data scientist. FOKE may recommend the following learning
path:

> Step 1: Take the "Introduction to Data Science" course to learn the basics
> of data manipulation, visualization, and machine learning. This course
> aligns well with your background in programming and statistics, and will
> provide a solid foundation for more advanced topics.
> Step 2: Enroll in the "Database Systems" and "Data Mining" courses to deepen
> your knowledge of data management and analysis. These courses build upon the
> concepts you learned in the introductory course and are highly relevant to
> your career goal.
> Step 3: Gain practical experience by participating in data science projects
> or internships. Based on your performance in the previous courses and your
> interest in healthcare, we recommend joining the "Medical Image Analysis"
> project led by Prof. Smith. This project will help you apply your skills to
> real-world problems and enhance your resume.

By providing personalized and explainable learning path recommendations, FOKE
can help learners navigate the complex course space, discover their interests
and strengths, and achieve their learning and career goals more effectively
and efficiently. The interactive and dynamic nature of the recommendation
process also allows learners to actively shape their learning experience and
adapt to their changing needs and preferences.

###  4.3 Intelligent Homework Assessment

Writing assessment(A Example of Homework Assessment) is a time-consuming and
subjective task that requires teachers to provide detailed and constructive
feedback to help students improve their writing skills. FOKE can be applied to
automate the writing assessment process and generate objective, comprehensive,
and actionable feedback.

The technical workflow of FOKE in writing assessment is as follows:

  1. 1.

The knowledge forest of writing aspects, criteria, and examples is constructed
from the rubrics, writing prompts, and exemplar essays.

  2. 2.

The user profile of each student is built based on their writing history,
performance, and feedback.

  3. 3.

For each writing assignment, FOKE generates a set of prompts to elicit the
student’s self-assessment and reflection on their writing strengths and
weaknesses.

  4. 4.

The student’s essay is analyzed using natural language processing techniques
to identify the key aspects such as coherence, argument, and grammar. The
results are compared with the writing criteria and examples in the knowledge
forest to generate an evaluation report.

  5. 5.

The evaluation report is presented to the student in an interactive format,
with detailed feedback, suggestions, and examples for each aspect of their
writing. The student can explore the feedback, ask questions, and revise their
essay accordingly.

For example, suppose a student submits an argumentative essay on the topic of
"social media and mental health". FOKE may generate the following feedback
report:

> Aspect 1: Thesis Statement Your thesis statement is clear and concise,
> stating your main argument that social media has negative impacts on mental
> health. However, you could make it more specific by mentioning the key
> points you will use to support your argument, such as addiction, self-
> esteem, and cyberbullying.
>
> Aspect 2: Evidence and Examples You provided some relevant evidence to
> support your argument, such as the study showing the correlation between
> social media use and depression. However, your examples are mostly anecdotal
> and lack credibility. Consider using more research findings or expert
> opinions to strengthen your case.
>
> Aspect 3: Counterargument and Rebuttal You mentioned a counterargument that
> social media can also have positive effects on mental health, such as
> connecting people and providing support. However, your rebuttal is weak and
> does not effectively address the counterargument. You could argue that the
> negative effects outweigh the positive ones, or provide evidence to show the
> limitations of the counterargument.

By generating objective, detailed, and actionable feedback on various aspects
of writing, FOKE can help students identify their strengths and weaknesses,
understand the criteria and expectations, and improve their writing skills
through iterative revision and reflection. The interactive and personalized
nature of the feedback also facilitates student-teacher communication and
collaboration, making the writing assessment process more efficient,
effective, and engaging.

##  5 Scholar Hero: Empowering Education with FOKE

Scholar Hero is a pioneering AI-driven educational application that embodies
the core principles and innovations of the FOKE framework. By seamlessly
integrating advanced AI agents and RAG (Retrieval-Augmented Generation)
technologies, Scholar Hero aims to revolutionize the traditional education
landscape and bring tangible benefits to every student’s learning journey and
campus life.

###  5.1 System Architecture and Workflow

The architecture of Scholar Hero is built upon the FOKE framework, leveraging
its hierarchical knowledge representation, multi-dimensional user profiling,
and structured prompt generation.

The system consists of three main components: 1) Knowledge Base Construction,
2) User Profiling and Personalization, and 3) Interactive Learning Services.

In the Knowledge Base Construction phase, Scholar Hero employs advanced data
mining and knowledge extraction techniques to build a comprehensive and
hierarchical knowledge base covering a wide range of educational topics and
campus-related information. The knowledge base is continuously updated and
expanded to ensure its relevance and accuracy.

The User Profiling and Personalization component is responsible for creating
and maintaining multi-dimensional user profiles based on students’ learning
preferences, academic performance, interests, and behavioral data. These
profiles enable Scholar Hero to deliver highly personalized content,
recommendations, and services tailored to each individual student’s needs.

The Interactive Learning Services component is the core of Scholar Hero,
powered by AI agents and RAG technology. It provides a wide array of
intelligent educational services, including personalized course
recommendations, interactive tutoring, intelligent homework assistance, and
AI-generated learning materials. The AI agents engage in natural language
interactions with students, answering their questions, providing explanations,
and offering guidance and support throughout their learning process.

###  5.2 Unique Features and Innovations

Scholar Hero distinguishes itself from traditional educational applications
through its unique features and innovations, which are deeply rooted in the
FOKE framework:

  * •

Personalized Learning Paths: Scholar Hero generates dynamic and personalized
learning paths for each student based on their knowledge level, learning
style, and goals. The system continuously adapts and optimizes these paths as
students progress, ensuring a truly personalized learning experience.

  * •

Interactive and Engaging Learning: The AI agents in Scholar Hero provide
interactive and engaging learning experiences through natural language
conversations, real-time feedback, and multimedia content. Students can ask
questions, seek clarifications, and receive instant support, fostering a more
active and immersive learning environment. Figure 4 demonstrates a student
interacting with an AI agent in Scholar Hero.

Figure 3: A student interacting with an AI agent in Scholar Hero Figure 4:
teacher setting an AI agent in Scholar Hero. Figure 5: teacher setting the
Knowledge Forest of AI agent.

  * •

Intelligent Homework Assistance: Scholar Hero offers intelligent homework
assistance by leveraging the knowledge base and AI agents. It can
automatically generate explanations, provide step-by-step guidance, and offer
personalized feedback on students’ assignments, helping them to identify their
strengths and weaknesses and improve their understanding of the subject
matter.

  * •

Seamless Integration with Campus Life: Scholar Hero goes beyond academic
learning by integrating various aspects of campus life into its services. From
finding the best food options on campus to managing schedules and
extracurricular activities, Scholar Hero serves as a comprehensive and
intelligent companion for students throughout their university journey.

  * •

Continuous Learning and Improvement: Scholar Hero is designed to continuously
learn and improve through user interactions and feedback. The system employs
advanced machine learning algorithms to refine its knowledge base, user
profiles, and recommendation models, ensuring its services remain accurate,
relevant, and up-to-date.

###  5.3 Real-World Impact and Future Directions

Scholar Hero has been successfully deployed in several pilot universities,
receiving enthusiastic feedback from students and educators alike. The
application has demonstrated significant potential in enhancing student
engagement, improving academic performance, and promoting a more personalized
and effective learning experience.

As we continue to develop and expand Scholar Hero, we envision a future where
AI-driven educational applications become an integral part of the educational
ecosystem. By leveraging the power of the FOKE framework and the lessons
learned from Scholar Hero, we aim to create more intelligent, adaptive, and
inclusive educational solutions that cater to the diverse needs of learners
worldwide.

Furthermore, we plan to conduct extensive user studies and evaluations to
assess the long-term impact of Scholar Hero on student learning outcomes,
satisfaction, and overall educational experience. The insights gained from
these studies will not only inform the future development of Scholar Hero but
also contribute to the broader research community in AI-driven education.

In conclusion, Scholar Hero stands as a testament to the transformative
potential of the FOKE framework in reshaping the educational landscape. By
harnessing the power of AI, we are paving the way for a new era of
personalized, engaging, and effective learning experiences that empower
students to thrive in their academic and personal lives.

##  6 Conclusion and Future Work

In this work, we proposed a novel framework named FOKE that integrates
foundation models and knowledge graphs for intelligent and personalized
education. FOKE leverages the power of large language models and domain
knowledge to generate adaptive learning support, such as interactive prompts,
personalized feedback, and explainable recommendations. Through a
comprehensive analysis of three representative application scenarios, we
demonstrated the potential of FOKE in enhancing the effectiveness, efficiency,
and engagement of various educational tasks, such as programming education,
learning path planning, and writing assessment.

The key contributions of this work include:

  * •

A flexible and extensible framework that incorporates foundation models and
knowledge graphs to capture the complex relationships among learning content,
learner characteristics, and pedagogical strategies;

  * •

A set of innovative techniques, such as prompt engineering, knowledge forest
construction, and explainable recommendation, to generate personalized and
interactive learning support;

  * •

A systematic analysis of the application scenarios, workflows, and examples of
FOKE in three important educational domains, showcasing its wide applicability
and practical value.

Our work explores a promising direction in developing next-generation AI-
powered educational systems that can adapt to the diverse needs and
preferences of learners, and provide intelligent and engaging learning
experiences at scale. The generic and modular design of FOKE also makes it
potentially applicable to other educational contexts, such as corporate
training, online learning, and lifelong learning. By leveraging the rapid
advancement of foundation models and knowledge graphs, FOKE can continuously
evolve and improve its capabilities in understanding learners, knowledge, and
pedagogy.

As a concrete application of the FOKE framework, we have developed an
intelligent educational system named "Scholar Hero". Scholar Hero is designed
to provide personalized and interactive learning support for various subjects
and domains, with a special focus on data science education. Currently,
Scholar Hero is being piloted in an introductory data science course at the
Communication University of China, where it serves as a virtual teaching
assistant to provide adaptive learning materials, formative assessments, and
real-time feedback to students.

The development and deployment of Scholar Hero is an ongoing process, with
continuous iterations and improvements based on the feedback and data
collected from the pilot study. Preliminary results show that Scholar Hero has
the potential to improve students’ learning outcomes, engagement, and
satisfaction, by providing them with tailored and timely support that caters
to their individual needs and progress. We plan to conduct more rigorous and
large-scale evaluations of Scholar Hero in the future, and explore its
application in other educational settings and domains.

Despite the promising progress, our work still has several limitations and
challenges. First, the knowledge forest and user profiles in Scholar Hero are
currently constructed based on limited and domain-specific data, which may not
capture the full complexity and diversity of learners and subjects. Second,
the prompt engineering and recommendation techniques used in Scholar Hero are
still rudimentary and need to be further optimized for efficiency and
effectiveness. Third, the long-term impact and generalizability of Scholar
Hero need to be validated through longitudinal and cross-domain studies.

Therefore, we plan to pursue the following directions in our future work:

  * •

Expand the knowledge forest and user profiling capabilities of Scholar Hero by
incorporating more diverse and large-scale educational data, such as learning
resources, assessments, and student records;

  * •

Develop more advanced and scalable techniques for prompt engineering and
recommendation, leveraging the state-of-the-art research in natural language
processing, knowledge representation, and recommender systems;

  * •

Conduct more comprehensive and long-term evaluations of Scholar Hero, both in
the current pilot study and in other educational contexts, to assess its
effectiveness, usability, and generalizability;

  * •

Investigate the pedagogical and ethical implications of using AI-powered
educational systems like Scholar Hero, such as the alignment with learning
theories, the fairness and transparency of the generated support, and the
impact on teacher-student relationships.

We believe that our work on the FOKE framework and its application in Scholar
Hero represents an important step towards realizing the full potential of AI
in education. By integrating foundation models, knowledge graphs, and
educational domain knowledge, we can create intelligent and personalized
learning experiences that can benefit learners of diverse backgrounds and
abilities. We hope that our work can inspire more research and development
efforts in this direction, and contribute to the advancement of AI-powered
education.

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