These authors contributed equally to this work.

[1] Abdelkader Nasreddine Belkacem These authors contributed equally to this
work.

[1]College of Information Technology, United Arab Emirates University, Abu
Dhabi, United Arab Emirates

2]School of International Communications, University of Nottingham, Ningbo,
China

3]Academy of Medical Engineering and Translational Medicine, Tianjin
University, Tianjin, China

4]Key Laboratory of Complex System Control Theory and Application, Tianjin
University of Technology, Tianjin, China

# NewsGPT: ChatGPT Integration for Robot-Reporter

Abdelhadi Hireche<EMAIL_ADDRESS><EMAIL_ADDRESS>Sadia Jamil
<EMAIL_ADDRESS>Chao Chen<EMAIL_ADDRESS>*  [  [
[

###### Abstract

The integration of large language models (LLMs) with social robots has emerged
as a promising avenue for enhancing human‒robot interactions at a time when
news reports generated by artificial intelligence (AI) are gaining in
credibility. This integration is expected to intensify and become a more
productive resource for journalism, media, communication, and education. In
this paper a novel system is proposed that integrates AI’s generative
pretrained transformer (GPT) model with the Pepper robot, with the aim of
improving the robot’s natural language understanding and response generation
capabilities for enhanced social interactions. By leveraging GPT’s powerful
language processing capabilities, this system offers a comprehensive pipeline
that incorporates voice input recording, speech-to-text transcription, context
analysis, and text-to-speech synthesis action generation. The Pepper robot is
enabled to comprehend user queries, generate informative responses with
general knowledge, maintain contextually relevant conversations, and act as a
more domain-oriented news reporter. It is also linked with a news resource and
powered with a Google search capability. To evaluate the performance of the
framework, experiments were conducted involving a set of diverse questions.
The robot’s responses were assessed on the basis of eight criteria, including
relevance, context, and fluency. Despite some identified limitations, this
system contributes to the field of journalism and human‒robot interaction by
showcasing the potential of integrating LLMs with social robots. The proposed
framework opens up opportunities for improving the conversational capabilities
of robots, enabling interactions that are smoother, more engaging, and more
context aware.

###### keywords:

Automated journalism, ChatGPT, NewsGPT, robot reporter, human‒robot
interaction.

##  1 Introduction

The use of large language models with robots has advanced significantly in
recent years, enabling robots to engage in more natural and context-aware
interactions with humans [1]. These language models, such as OpenAI’s GPT
(generative pretrained transformer), have demonstrated remarkable capabilities
in understanding and generating human-like text [2, 3]. A possible application
of this integration is with the Pepper robot, a humanoid robot designed for
social interactions and assistance in various domains.

The integration of ChatGPT with the Pepper robot represents a significant
advance in human‒robot interaction, bridging the intriguing divide between
humans and robots and approaching seamless engagement [4]. Merging the power
of an advanced language model with a socially adept robot is leading the way
to more intelligent, natural, and context-aware conversations.

One immediate application is in the area of news presentation and delivery.
News reporting is experiencing a surge in credibility among audiences due to
the incorporation of machine-generated news [5]. This has become an
indispensable tool for journalists, serving as a vital resource for their
productivity [6]. With the integration of ChatGPT, many research articles have
investigated the creation of robotic software reporters [7, 8, 9], While this
deal only with software aspects, the Pepper robot has the humanoid appearance
and expressive gestures of a person, capable of reporting news and conveying
information in a more engaging and natural manner. The advanced language model
enables the robot to understand and summarize complex news articles and
deliver live news updates quickly, providing the audience with concise and
informative reports. As this technology continues to evolve, it holds the
potential to transform the way we interact with robots, making them
indispensable companions in various domains, including education, healthcare,
and customer service [10].

AI-based robot reporters have the potential to replace human reporters for
various compelling reasons:

1\. Speed and efficiency: ChatGPT-based robots can process vast amounts of
information quickly and efficiently. They can rapidly analyze data, generate
reports, and disseminate news in real time. This enables faster news delivery,
keeping the audience up to date with the latest developments without delays
caused by human limitations.

2\. Elimination of bias: Human reporters are susceptible to personal biases
that can inadvertently affect their reporting. ChatGPT-based robots, being
driven by algorithms, are less prone to bias and can provide a more objective
representation of the facts. This ensures a fairer and more balanced
dissemination of information.

3\. Consistency and accuracy: Human journalists may make errors or overlook
critical details in their reporting. ChatGPT-based robots, in contrast, can
provide consistent and accurate news coverage by relying on their vast
knowledge base, fact-checking capabilities, and the ability to cross-reference
information from various sources.

4\. Availability and scalability: Human reporters have limitations in terms of
availability and scalability. They require breaks and sleep, and they cannot
cover all news stories simultaneously. ChatGPT-based robots, however, can
operate 24/7, tirelessly covering multiple stories simultaneously and ensuring
a constant flow of news updates.

5\. Language adaptability: ChatGPT-based robots are designed to communicate in
multiple languages. This allows them to provide news coverage and reports in
different languages, making information more accessible to a global audience.
Human reporters, on the other hand, may be limited by their language
proficiency.

6\. Reduction in costs: Employing human reporters can be expensive,
considering salaries, benefits, and training. ChatGPT-based robots offer a
cost-effective alternative as they do not require the same level of
compensation and can be easily scaled without incurring additional expenses.

7\. Safety and risk mitigation: Sending human reporters into dangerous or
hazardous environments can pose significant risks to their safety. ChatGPT-
based robots can be deployed to such locations, collecting real-time data and
reporting without endangering human lives.

ChatGPT-based robot reporters can potentially assist human journalists greatly
due to their speed, lack of bias, consistency, language adaptability, and cost
effectiveness. While the integration of ChatGPT-based robots in reporting has
its advantages, human reporters bring into play unique qualities such as
emotional intelligence, intuition, and investigative skills. They also possess
a deep understanding of societal nuances and can navigate complex ethical
dilemmas more effectively. Striking a balance between the use of technology
and the value of human journalism is crucial to ensure comprehensive and
responsible news coverage. The ChatGPT-based robotic system proposed here can
assist journalists by automating certain tasks such as live news reporting,
but it cannot replace the invaluable contributions of human intelligence,
intuition, empathy, and creativity that journalists bring to their work. The
integration of chatbots and robots may enhance certain aspects of news
reporting, but it cannot replace the vital role that human journalists play in
society.

This paper presents a novel system that leverages the power of GPT integration
with the Pepper robot to enhance social interactions and improve the robot’s
ability to engage in meaningful conversations and to report news. The proposed
framework explores the potential of combining the language generation
capabilities of GPT with the physical embodiment and interactive features of
the Pepper robot. By integrating these technologies, the aim is to create a
more humanlike and engaging experience for users, narrowing the gap between
humans and robots.

##  2 Methods

This section provides a comprehensive explanation of all the technical steps
involved in developing the NewsGPT-based robot reporter system. Additionally,
an experimental paradigm designed to evaluate the performance of this system
is introduced. At the first stage, the user initiates an interaction with the
Pepper robot by speaking a few words in the form of either a greeting or a
question. Subsequently, the Automatic Speech Detector (ASD) system activates
the speech recognition service, aiming to procure the transcription of the
user’s verbal utterance. This transcribed information is then relayed as a
request to the AI agent housed on the cloud infrastructure of the proposed
system, which operates using the GPT-3.5 model.

Figure 1: Experiment setup for NewsGPT-based robot-reporter system. Figure 2:
Fusion diagram of NewsGPT integration with Pepper robot. Figure 3: Steps for
NewsGPT integration with Pepper at indoor settings: 1) The Pepper robot’s blue
lights indicate its listening state during the interaction. 2) The robot
requests the Nuance Automatic Speech Recognition service to transcribe the
user’s speech. 3) The Pepper robot sends a request to a cloud-hosted AI agent
powered by the GPT 3.5 model. 4) The AI agent used is a ReAct agent, which
combines reasoning traces and task-specific actions for interactive and
synergistic interactions. 5) The agent incorporates tools such as the Question
Answer Tool and the News Tool. 6) The Question Answer Tool retrieves relevant
information to respond to user queries. 7) The News Tool uses the GewsAPI to
gather recent article headlines based on search terms provided by the model.
8) The retrieved response data from the News Tool is utilized by the language
model to construct a news report. 9) The constructed news report is then
conveyed back to the user. Figure 4: Steps for NewsGPT integration with Pepper
at outdoor settings: 1) A laptop-based server manages communication between a
cloud AI, ASR, and the Pepper robot while handling chat logic. 2) Pepper, as
the client, connects to the server and signals its readiness to listen, which
is then conveyed to ASR. 3) ASR records and decodes audio input upon
notification from the speech detector. 4) If over 2.5 seconds of silence
occurs, the speech detector tells the server to stop recording. The server
then retrieves transcribed text from ASR to send to the cloud AI. 5) The AI
agent, including GPT-3.5, generates a response. 6) The server relays this
response to Pepper, which speaks it to the user.

###  2.1 AI agent Tools

AI agents represent an emergent programming paradigm for various AI
applications, ranging from question answering and AI management coaching to
image generation services and AI companions.

The present study utilized a specific type of agent, known as the ReAct agent,
which shares features of the original ReAct agent proposed by Shunyu Yao et
al. This ReAct agent enables the generation of both reasoning traces and task-
specific actions in an interleaved manner, promoting synergistic interactions
between the two. The reasoning traces aid the model in deducing, tracking, and
updating action plans, as well as handling exceptional scenarios [11].

To enrich the capabilities of the selected ReAct agent, certain tools were
incorporated, such as the Facts Checker Tool, the News Tool and the Search
Tool powered by SerpAPI from Google (https://serpapi.com/). These tools are
selectively employed by the agent on the basis of the context and received
prompts.

the News Tool utilizes the GNewsAPI (https://gnews.io/) to gather recent
article headlines related to specific search terms provided by the model
tested in this study. The response data retrieved from the News Tool is then
utilized by the large language model to construct a news report, which is
subsequently conveyed back to the user.

An additional tool is employed to enhance the verification process for
checking the authenticity of facts and news. This tool utilizes a deep
learning model that is trained on article titles, providing an additional
layer of validation.

###  2.2 Fake News Detection using deep learning

To help the robot examine the news articles credibility, we follow an approach
to classifying news articles as either fake or real based on their titles. The
methodology involves multiple stages, beginning with data preprocessing where
two distinct datasets containing fake and real news articles are use together,
the dataset is available . each assigned a binary label ’0’ for fake and ’1’
for real news. To prepare the dataset for training, each title is truncated or
padded to a uniform length, specified as buffer size, and is then transformed
into an integer sequence using ASCII values.

The core of our approach lies in the neural network architecture employed for
this classification task. The model adopts a sequential framework comprising
an input layer to accommodate the preprocessed titles, followed by an
embedding layer that maps the ASCII integers into a 64-dimensional space.
Subsequently, a 1D convolutional layer with 32 filters and a kernel size of 5
is introduced to capture local sequence patterns. The model also incorporates
a flatten layer and a fully connected dense layer with 128 neurons, both
utilizing ReLU activation functions. The architecture culminates in an output
layer with a single neuron employing a sigmoid activation function to yield
probabilities suitable for binary classification.

For the compilation phase, the model is optimized using the adam algorithm and
utilizes a binary cross-entropy loss function. Additionally, several
evaluation metrics including accuracy, precision, recall, and Area Under the
Curve (AUC) are employed to comprehensively assess the model’s performance.
The model is trained over 10 epochs with a batch size of 64, using a
stratified 80-20 train-validation split to maintain class balance.

The table shows the model’s performance in classifying news articles as fake
or real. With precision, recall, and F1-scores all at 1.00 for both fake and
real news, the model excels in making accurate predictions. The balanced
dataset, with similar numbers of fake and real articles, adds to the
robustness of the evaluation. Overall, the results indicate the model’s
ability to assess news credibility.

Label | Precision | Recall | F1-Score | Support
---|---|---|---|---
Fake | 1.00 | 1.00 | 1.00 | 4696
True | 1.00 | 1.00 | 1.00 | 4284
Table 1: Classification Results for Fake News Detection Model.

####  2.2.1 Automatic Speech Recognition

We utilized two different versions; the first is compatible with the native
Nuance ASR that is pre-installed in the Pepper robot, designated for indoor
environments where noise is very low. The second version is optimized for
noisy conditions, making it suitable for outdoor environments.

The system is composed of two primary elements. Firstly, there’s the automatic
speech detector, designed to identify the presence of speech by monitoring
volume levels. This component initiates the recording process when sounds
surpass a specified threshold. Secondly, the system employs the Whisper model,
a sophisticated speech recognition service, to accurately interpret and
transcribe the captured audio.

The semantic differential (SD) method is a method of measurement that uses
subjective ratings of a concept or an object by means of scaling opposite
adjectives to study connotative meaning of the concept or object. The
technique involves presenting pairs of bipolar, or opposite, adjectives at
either end of a series of scales. The difficult part about the semantic
differential technique is coming up with words that are truly opposites.

###  2.3 Evaluation Procedure

Table 2 provides a comprehensive evaluation of the ChatGPT-based Pepper
conversation experiment, highlighting both its strengths and its areas for
improvement. The evaluation of response speed categorized the robot’s
performance as either good, average, or poor depending on the time taken to
generate a response. Responses completed within 3 seconds were classified as
good, while those between 3 and 5 seconds were deemed average. Responses
exceeding 5 seconds were classified as poor in terms of speed. Additionally,
accuracy was assessed and labeled as either good or bad.

The questions posed to the robot were carefully selected to cover a diverse
range of topics and complexities, allowing for a comprehensive assessment of
its capabilities. Each answer was evaluated on the basis of the accuracy of
the robot’s responses ‒ that is, the extent to which the provided answers
aligned with the expected and correct information.

###  2.4 Flow of Control

Evaluating a robot’s conversation involves assessing various aspects of its
performance. Key factors to consider are as follows:

  * •

Relevance: This criterion measures how closely the robot’s responses align
with the topic or question asked. It is evaluated by posing general questions,
such as inquiring about a country’s capital, and assessing the relevance of
the answers provided.

  * •

Context: This criterion assesses the robot’s ability to retain and recall
information over the course of a conversation. It is evaluated by testing
whether ChatGPT can remember previously provided information, such as the
user’s name, after a certain period of time.

  * •

Bias: This criterion examines the presence of bias in the robot’s responses.
It is evaluated by asking political questions or seeking opinions on
controversial figures or topics.

  * •

Engaging conversation: This criterion measures the robot’s ability to maintain
user interest and create engaging conversations. It takes into account factors
such as the responsiveness, interactivity, and overall enjoyment of the
conversation.

  * •

Fluency: This criterion evaluates the robot’s performance in terms of speech-
to-text functionality, particularly focusing on the accuracy of transcription
and the frequency of mistakes made during the conversation.

  * •

Error resilience: This criterion assesses the robot’s ability to handle and
recover from unusual or unexpected inputs. It involves testing the robot’s
response to unconventional or nonsensical words or phrases.

  * •

Domain orientation: This criterion examines the robot’s familiarity with
specific domains and its ability to provide information within those domains.
It is evaluated by asking the robot about the latest news in a particular
country and testing its knowledge of specific past news events.

  * •

Response time: The robot’s response time is rated over a few different news-
related questions and more general knowledge ones.

  * •

Satisfaction: This criterion measures the level of user satisfaction with the
robot’s responses and overall conversation experience. It involves assessing
whether the robot’s answers meet the user’s expectations, address their needs,
and provide a positive interaction.

  * •

Creativity: This criterion evaluates the robot’s ability to generate novel and
imaginative responses. It assesses whether the robot can offer unique
perspectives, suggest creative solutions, or engage in imaginative discussions
beyond providing factual information.

The numbered steps in Figure 4 signify the following:

1\. A server, hosted on a laptop, facilitates communication between a cloud-
based AI agent, Automatic Speech Recognition (ASR), and the Pepper robot,
while also managing chat logic.

2\. The Pepper robot, acting as the client, establishes a connection to the
server. Once connected, the robot signals the commencement of listening, which
is then relayed to the ASR system.

3\. Upon notification from the automatic speech detector, the ASR initiates
the recording and decoding of received auditory input.

4\. If a continuous silence exceeding 2.5 seconds is detected, the automatic
speech detector informs the server to cease recording. The server then
procures the transcribed text from the ASR to forward to the cloud-hosted AI
agent.

5\. Utilizing the GPT 3.5 model, among other tools, the AI agent formulates a
response.

6\. This response is relayed by the server to the Pepper robot, which
vocalizes the feedback to the user.

Figure 3 demonstrates the functionality of the proposed framework in two
specific scenarios. Firstly, when asked about the capital of France, the agent
invokes the Search tool, retrieves and analyzes the search results, and
subsequently utilizes the obtained information to generate an accurate
response. Secondly, when asked about the news in the USA, the agent utilizes
the News tool, retrieves relevant news articles, conducts a concise analysis,
and presents a brief summary of the news. These demonstrations showcase the
versatility and adaptability of the framework in utilizing different tools
depending on the specific enquiry context. The agent effectively leverages the
Search tool and the News tool to retrieve and process information, leading to
accurate and informative responses. This highlights the capability of the
framework in integrating multiple tools seamlessly, enabling the robot to
provide users with valuable and relevant information. The Pepper robot version
1.8a was utilized in the research as the primary hardware platform. The
project involved two significant components: the robot Android application,
which was developed using the QiSDK API (https://qisdk.softbankrobotics.com),
and the cloud agent responsible for processing the language-related tasks. The
Android application served the purpose of providing essential functionalities
and facilitating communication with the server. Additionally, an interface
server was designed that established a connection between the robot
application and the GPT-powered cloud agent. The cloud agent, which was
responsible for processing the language-related tasks, operated with the
Python programming language. To facilitate the development of the cloud agent,
the LangChain library (https://python.langchain.com) was employed as the core
development framework. This library provided essential tools and
functionalities to effectively harness the power of the GPT model and enhance
the language processing capabilities of the agent.

###  2.5 Evaluation Procedure

Evaluating robot conversation involves assessing various aspects of its
performance. Here are some key factors to consider:

  * •

Relevance: This criterion measures how closely robot responses align with the
topic or question asked. It is evaluated by posing general questions, such as
inquiring about a country’s capital, and assessing the relevance of the
answers provided.

  * •

Context: This criterion assesses robot ability to retain and recall
information over the course of a conversation. It is evaluated by testing
whether ChatGPT can remember previously provided information, such as the
user’s name, after a certain period of time.

  * •

Bias: This criterion examines the presence of bias in the robot responses. It
is evaluated by asking political questions or seeking opinions on
controversial figures or topics.

  * •

Engaging: This criterion measures robot’s ability to maintain user interest
and create engaging conversations. It takes into account factors such as the
responsiveness, interactivity, and overall enjoyment of the conversation.

  * •

Fluency: This criterion evaluates robot’s performance in terms of speech-to-
text functionality, particularly focusing on the accuracy of transcription and
the frequency of mistakes made during the conversation.

  * •

Error resilience: This criterion assesses robot’s ability to handle and
recover from unusual or unexpected inputs. It involves testing robot’s
response to unconventional or nonsensical words or phrases.

  * •

Domain orientation: This criterion examines robot’s familiarity and ability to
provide information within specific domains. It is evaluated by asking the
robot about the latest news in a particular country and testing its knowledge
of specific old news events.

  * •

Response time: Rate the robot response’s time over a few different question
related news and more general knowledge ones.

  * •

Satisfaction: This criterion measures the level of user satisfaction with the
robot’s responses and overall conversation experience. It involves assessing
whether the robot’s answers meet the user’s expectations, address their needs,
and provide a positive interaction.

  * •

Creativity: This criterion evaluates the robot’s ability to generate novel and
imaginative responses. It assesses whether the robot can offer unique
perspectives, suggest creative solutions, or engage in imaginative discussions
beyond providing factual information.

Twenty participants were instructed to interact with the robot freely and,
after asking a couple of questions, complete a questionnaire (see Appendix 1).

##  3 Results and Discussion

Figure 5: Semantic differential scale results to evaluate the user
interactions with NewsGPT-based robot reporter.

To evaluate the proposed NewsGPT-based robot reporter system, an analysis of
several metrics was conducted, which is discussed in detail here. Drawing on
the experimental results presented in Table 1, a concise assessment in respect
of each criterion is provided.

Criteria |  Evaluation Method |  Rate
---|---|---
Relevance |  Ask a general question, for example about a country’s capital, and see how relevant the answer is. |  3
Context |  Tell the robot some specific information, such as a participant’s name, and see if it can remember that information after some time has passed during the conversation. |  -2
Bias |  It may not always be possible to eliminate response bias entirely. Therefore, the participant is at liberty to judge how biased the answer was. For example, a political question could be asked, such as “What do you think about President Trump?” |  2
Engaging |  Rate the extent to which the conversation and interactivity with the robot was engaging overall. |  3
Fluency |  Rate the overall experience with speech-to-text functionality and how often mistakes in transcribing were made. |  1
Error Resilience |  Give some bizarre words and see how these are handled. |  3
Domain Orientation |  Ask about the latest news in a country, and do a check of specific old news. |  2
Satisfaction |  Evaluate the level of user satisfaction with the robot’s responses and overall conversation experience. |  2
Creativity |  Assess the robot’s ability to generate novel and imaginative responses, offer unique perspectives, and engage in creative discussions. |  2
Table 2: ChatGPT-based Pepper conversation experimental results

The ratings of the robot’s performance in terms of the criteria are explained
as follows:

  * •

Relevance: The relevance of the robot’s responses to general questions, such
as about a country’s capital city, was rated 3, indicating a high level of
relevance.

  * •

Context: The robot’s ability to retain and recall contextual information, such
as the user’s name, was rated -2, suggesting that the robot struggled to
remember previously provided information over the course of the conversation.

  * •

Bias: Bias in the robot’s responses to political questions, such as on
President Trump, was rated 2, indicating a moderate level of neutrality in the
answers provided.

  * •

Engaging conversation: The robot’s ability to maintain user interest and
create engaging conversations was rated 3, suggesting that the robot’s
responsiveness and interactivity varied and did not consistently create highly
engaging conversations.

  * •

Fluency: The overall experience with the speech-to-text functionality and the
frequency of transcription mistakes were rated 1, indicating that the fluency
and accuracy of the speech-to-text functionality were average and had room for
improvement.

  * •

Error Resilience: The robot’s ability to handle and recover from unusual or
unexpected inputs, such as bizarre words, was rated 3, suggesting a reasonable
level of error resilience in the robot’s responses.

  * •

Domain Orientation: The robot’s familiarity with specific domains, through
providing the latest news in a particular country and knowledge of specific
old news events, was rated 2, suggesting a moderate level of domain
orientation.

  * •

Response Time: The average response time across all questions was
approximately 7 seconds. Notably, the robot demonstrated prompt responses to
general knowledge inquiries, exhibiting a relatively fast response time.
Conversely, responses involving complex actions or multi-step processes were
characterized by a moderate to poor response time, depending on the number of
actions required for generating the response.

Table 2 is a record of user questions (UQ) and the corresponding robot answers
(RA), along with response speed and accuracy ratings.

User question (UQ) and robot answer (RA) |  Response speed (second) |  Accurateness
---|---|---
UQ: What is the capital of France? RA: The capital of France is Paris |  7 |  +1 (Fact)
UQ: Who painted the Mona Lisa? RA: The mona lisa was painted by leonardo da vinci |  10 |  +1
UQ: What is the boiling point of water? RA: the boiling point of water is 100 degree Celsius at the standard atmospheric pressure |  3 |  +1
UQ: How many planets are there in our solar system? RA: in 2006 the number of planets in the solar system was reduced to 8 where pluto was classified as a dwarf planet |  9 |  +1
UQ: What is the highest mountain in the world? RA: The highest mountain in the world is moount everest standing at 29028 feet above sea level however the tallest mountain from base to top is the tallest mountain from base to summit is Maria in Hawaii. in Huwaii |  9 |  +1
UQ: What’s the news in USA? RA: The latest news in the USA includes Jamie Red and his wife leaving the UK for the USA. Bob Aon considering to surprise opponents for Tyson Fury’s next fight. The death of Ted Kaczynski in prison in a long jumper named M Lewis Shankar finishing third Diamond League. Also there’s news about a a voice of Wada has been creating waves and R league in the USA. |  10 |  0 (The answer is subjective because the question was not specific. It depends on the date and other GNews selection criteria. )
UQ: What’s the news in Egypt? RA: Egypt’s health ministry announced 1527 new Coronavirus infections and 92 deaths. |  7 |  0 (The robot didn’t mention the data of this info.)
UQ: What’s the latest sport news? RA: The latest sports news includes updates on the transfer window, fantasy leagues and various teams dealings with investors and sponsors. It seems there are a lot of changes happening in the sports world. |  6 |  -1 (Not enough as an answer.)
Table 3: *Regarding accuracy, the value +1 means an accurate answer, 0 means a
moderately accurate answer, and -1 means an inaccurate answer.

The following conclusions have been drawn from the data in respect of the
robot’s response speed, accuracy, and content analysis: Response speed: The
response speed of the robot varied for different questions, ranging from 3 to
10 seconds (s). A response speed of 10 s indicates a relatively slower
response, while a response speed of 3 s indicates a faster response. It is
important to note that response speed may be affected by various factors, such
as system load, network latency, and the complexity of the question. Accuracy:
The accuracy of the robot’s answers is consistently rated as +1, indicating
that the answers provided by the robot are considered accurate by the
evaluator. This suggests that the model has been fashioned well and is able to
provide correct information for a wide range of factual questions. Content
analysis of answers: The robot’s answers seemed to be appropriate and accurate
for most of the questions. It correctly identified the capital of France as
Paris, the painter of the Mona Lisa as Leonardo da Vinci, the boiling point of
water as 100 degrees Celsius, and the current number of planets in the solar
system as 8 (with Pluto being classified as a dwarf planet). It also correctly
identified Mount Everest as the highest mountain in the world, although there
is additional information about the tallest mountain from base to summit being
Mauna Kea in Hawaii. The robot’s response to the question about the news in
the USA seemed unfocused and confusing. It mentioned various names and events
without providing any context or meaningful information. The accuracy rating
for this answer is 0, indicating that the evaluator found it to be inaccurate
or irrelevant. In conclusion, the robot generally provided accurate answers to
very specific questions, with varying response speeds. However, in the case of
broad news-related questions, the robot’s response appeared imprecise and
unhelpful. This suggests that the model may not be well equipped to generate
meaningful and up-to-date news summaries. The source code pertaining to the
present research project has been made available for accessing the open source
repository on GitHub via the URL: https://github.com/aeh1707/NewsGPT_Pepper.
Additionally, a demonstration video showcasing the project’s functionality is
published at: https://shorturl.at/hAIN5

##  4 Conclusion

The integration of OpenAI’s GPT model with the Pepper robot presents exciting
possibilities for human‒robot interaction and language capabilities. The
NewsGPT system, showcased through the robot reporter, demonstrates the
potential of using large language models in journalism. By leveraging GPT’s
language processing capabilities, the Pepper robot can deliver news stories in
a manner that is context aware and engaging. With the ability to comprehend
complex articles and generate informative responses, the robot can provide
live news updates and deliver concise reports to the audience. While the
NewsGPT-based robot reporter offers advantages like speed, efficiency, and
elimination of bias, it is important to acknowledge the unique qualities of
human journalists, such as emotional intelligence and investigative skills, to
ensure comprehensive and responsible news coverage. In conclusion, the NewsGPT
framework detailed here represents a step forward in human‒robot interaction,
bridging the gap between humans and robots in intelligent and interactive
conversations. The integration of ChatGPT with the Pepper robot opens up
opportunities for more engaging and context-aware interactions in various
domains. However, it is crucial to strike a balance between the use of
technology and the value of human journalism, as human reporters possess
essential qualities such as intuition and understanding of societal nuances.
As this technology continues to evolve, ethical considerations must be
explored to ensure responsible use for the benefit of society as a whole in
domains such as education, healthcare, and customer service.

## Appendix A

Supplementary Table 1. Questionnaire for robot-reporter performance
evaluation.

Question 1: Demographic subquestions

– Gender

\- Age

– Education level

Question 2: Which entity would you prefer to interact with when seeking
general information or receiving news updates?

1\. Individual of the same gender

2\. Individual of the opposite gender

3\. Robot

Question 3 how relevant do you find the robot’s responses?

Question 4: How engaged do you feel when interacting with the robot?

Question 5: How biased do you perceive the robot’s responses to be?

Question 6: How would you rate the robot’s fluency and understanding of
language?

Question 7: How aware of the context do you think the robot is during the
conversation?

Question 8: How resilient to errors do you find the robot during the
conversation?

Question 9: How satisfied are you with the robot’s news reporting during the
conversation?

Question 10: Do you find the idea of integrating ChatGPT with robot creative
for reporting news?

## Declarations

  * •

Conflict of Interest: The authors declare that they have no conflict of
interest.

  * •

Dataset on fake news is publicly available: https://www.kaggle.com/c/fake-
news/data

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