# Writing Tools: Looking Back to Look Ahead

Cerstin Mahlow  https://orcid.org/0000-0003-0215-5551 School of Applied
Linguistics, Zurich University of Applied SciencesWinterthurSwitzerland
<EMAIL_ADDRESS>
(2023)

###### Abstract.

Research on writing tools started with the increased availability of computers
in the 1970s. After a first phase addressing the needs of programmers and data
scientists, research in the late 1980s started to focus on writing-specific
needs. Several projects aimed at supporting writers and letting them
concentrate on the creative aspects of writing by having the writing tool take
care of the mundane aspects using NLP techniques. Due to technical limitations
at that time the projects failed and research in this area stopped. However,
today’s computing power and NLP resources make the ideas from these projects
technically feasible; in fact, we see projects explicitly continuing from
where abandoned projects stopped, and we see new applications integrating NLP
resources without making references to those old projects. To design
intelligent writing assistants with the possibilities offered by today’s
technology, we should re-examine the goals and lessons learned from previous
projects to define the important dimensions to be considered.

writing technology, natural language processing, intelligent writing tools,
interactive editing

††copyright: none††journalyear: 2023††conference: The Second Workshop on
Intelligent and Interactive Writing Assistants; April 23, 2023; Hamburg,
Germany††booktitle: The Second Workshop on Intelligent and Interactive Writing
Assistants, April 23, 2023, Hamburg, Germany††ccs: Human-centered computing
Text input††ccs: Human-centered computing User interface design††ccs: Human-
centered computing Graphical user interfaces††ccs: Human-centered computing
Hypertext / hypermedia††ccs: Applied computing Document preparation

##  1\. Where we come from

The term “word processing” first appeared in the 1960s, referring to a
combination of hardware and software [for the history of word processing
see,][]eisenberg1992,haigh2006. The first writing tools for microcomputers,
such as Electric Pencil or Easy Writer, were developed by hobby programmers;
they were soon displaced by commercial products (bergin2006a, 4). How people
actually used these new tools was not obvious. (rosson1983, 43) and
(whiteside1982, 57) therefore asked “How do people really use text editors?”
(title of (whiteside1982, 57)) in the early 1980s to gain insights for the
development of future editors. But even ten years later, after the failure of
a large project on writing support, (holt1992, 29) had to admit:

> In seems clear […] that in order to produce computer based tools to support
> writers and the writing process we must increase our knowledge of how
> writers conduct their craft. An increased understanding of writer’s
> requirements and the task involved in writing will form the basis of the
> next generation of writing tools. (holt1992, 29, X)

Projects like RUSKIN (williams1989, 62, 61), Writer’s Assistant (sharples1990,
48, 47), Intelligent Workstation (kempen1986, 31), and Editor’s Assistant
(dale1989, 13, 14) did not result in marketable products. They aimed to
improve (post-)editing and revision based on linguistic principles using
linguistic resources. The design and development, however, did not take into
account the real needs of users. Natural language processing (NLP) resources
were not yet mature enough to be used in real applications. The computing
power of PCs at the time was insufficient for real-time analysis—Editor’s
Assistant relied on constant full syntactic parsing of the growing text—and
generation and the resulting tools were thus too limited for practical use.

For RUSKIN, a post-editing support tool, (williams1990a, 58) notes that at the
time, most writing software was rather ad hoc implementations of ideas with
poor user interfaces incorporating checkers that provided inadequate or even
erroneous results (williams1990a, 58, 3). The project aimed to overcome the
poor quality of checkers by incorporating automatic syntax analysis—the
probabilistic parser only delivered results with a correctness of around 75%,
though (williams1993, 60, 117). Furthermore, the supposed “authors’ needs”
were based solely on the researchers’ intuitions. Only at the end of the
project they realized that real authors actually would have preferred support
_during_ writing, not in a separate post-editing phase, as “the concept of
postwriting software implies a linear model of the human writing process which
is at best simplistic and at worst may be completely misleading”
(williams1989, 62, 6). The first models of writing as a complex and non-linear
process had already been published at this point, e.g., by (flower1981, 20)
and (scardamalia1983, 44). Writer’s Assistant did take into account writing
research, but lacked an _implementable_ writing model (sharples1989, 47, 26).
This is still an open issue in writing research today, but only rarely
addressed [e.g.,][]hayes2001,hayes2012b. The prototype of Writer’s Assistant
(sharples1990, 48) focused on authors’ activities and aimed to be more than
just the next word processor: “The Writer’s Assistant is a computer-based
_cognitive support_ system for people who create complex documents as part of
their professional live” (sharples1989, 47, p. 22, emphasis in original).
Writer’s Assistant was supposed to be a combination of word processor, idea
processor, and outliner/structure editor to provide authors with different
views on different aspects on the text under development (sharples1989, 47,
22). But at that time, it could not be implemented and remained a thought
model.

Serious research on writing tools stopped in the late 1980s, once corporate
customers—including universities—had decided what to purchase
[see][]daiute1983,taylor1987,hawisher1988,ross1991,vernon2000 and Microsoft
Word had achieved monopoly status in the consumer market
[see][]eisenberg1992,bergin2006b,wohl2006. The main reason was that MS Word
was bundled with many PCs. From a customer’s perspective, everything was
ready: one could just turn on the new computer and start writing with MS Word.
Purchasing and installing another word processor such as WordPerfect would
have required purchasing another license and installing another program. This
ubiquity had several effects: writers became accustomed to the appearance,
features, and affordances of MS Word; the format of text files produced with
MS Word became the default file format expected and demanded for submissions
of academic theses and the like, for interchange between writers when writing
collaboratively, and for further processing in publishing houses.

The first effect led to the general assumption that any other new writing
facility, e.g. in the first learning management systems that appeared in the
early 2000s, should be designed to resemble the look and feel of MS Word and
include its main features to provide a familiar user experience. This also
applies to the first versions of Google’s web-based word processor Google
Docs, which became available in a beta version in early 2006. Experimental
projects such as the British Telecom-funded Editor’s Assistant had no progress
in sight in the 1990s to overcome technological obstacles (computing power,
quality of NLP) that would justify further investment.

The integration of NLP technology into word processors beyond checkers for
spelling and grammar has been a research topic since the 1980s
[e.g.,][]kempen1986,kempen1992, but did not result in commercial products
either. To overcome the challenges for parsers arising from what
(vandevanter1995, 51) calls “the three I’s”: _ill-formedness_ ,
_incompleteness_ , and _inconsistency_ of sentences during writing,
experimental word processors attempted to incorporate _syntax orientation_ as
derived from _syntax-oriented_ text editors such as EMILY (hansen1971, 24),
Cornell (teitelbaum1981, 50), PEN (allen1981, 1), JANUS (chamberlin1981, 8),
PARSE (chusho1983, 9), Mentor (gouge1983, 22, 33), PAN (ballance1992, 3, 53),
or CodeProcessor (vandevanter2000, 52). These editors handled documents as
tree structures and were implementations of programming principles like
_stepwise refinement_ and _structured programming_ (dijkstra1972a, 17, 63).
However, similar to programmers, writers objected to always produce complete,
well-formed sentences, as this was not compatible with their writing habits.
It also does not reflect the writing process as has been observed in various
studies: authors often start revising a sentence _before_ a complete first
version of this sentence is finished
[see][]olive2009,wengelin2009,leijten2010a. (dale1997, 12) predicted in 1997:

> The major developments in the next five to ten years are likely to be of an
> augmentative nature, with increasingly sophisticated systems that have
> people and machines doing what they each do best. The key here is to add
> intelligence and sophistication to provide _language sensitivity_ , enabling
> the software to see a text not just as a sequence of characters, but as
> words and sentences combined in particular structures for particular
> semantic and pragmatic effect. (dale1997, 12, p. 235, emphasis in original)

No such systems were available for the general public in the 2000s, though. At
that time, (mahlow2008a, 38) proposed language-aware functionality, but only
developed a proof of concept as extension to Emacs (mahlow2009e, 39). In
writing research, the influence of the writing tool and medium are only
occasionally acknowledged (sharples1996c, 45, 7, 36); the field concentrates
on cognitive aspects and writing strategies.

##  2\. Where we are now

Starting in the 2010s, the emphasis on writing experience, personalization of
tools, and the growing diversity of input devices (and methods) and displays
prompted the development of “new writing tools.” Their functionalities are
often working implementations of methods and concepts originally described and
developed in the 1960s and 1970s that used to be considered failures—but had
actually only failed due to the limitations of computers at that time. Only
now we see the inverse development, back to ideas and applications of the
1960s, when projects like NLS (oN-Line System) (engelbart1962, 19) where
“pushed aside in favor of computer systems more oriented toward print
practices” (ittersum2008, 54). NLS already combined functionality to write
text, messages similar to what later became known as e-mail, and “computer
conferencing” for allowing collaborative simultaneous editing of documents
(callender1982, 6). There was no fixed final document format—e.g., a printed
page—the focus was on facilitating online text production by implementing

> text editing capabilities of later word processors, including word wrap,
> search and replace, and scrolling, and the use of a mouse to select text to
> be cut and pasted between documents. Indeed Engelbart’s system was much more
> complex than most of subsequent word processing systems (haigh2006, 23, 21)

One type of functionality that (mahlow2008a, 38) suggest are information
functions that use NLP techniques to highlight certain aspects of the evolving
text, commonly referred to as “syntax highlighting.” Since 2013, iA Writer has
offered such a feature in a commercial product to specifically highlight
nouns, verbs, adjectives, etc., advertising it as “using parts of speech to
improve your writing” and explicitly stating that writers deserve the same
professional support as programmers.111https://ia.net/writer/support/writing-
tips/parts-of-speech The use of NLP has been feasible for some time now, both
in terms of quality and the computing power required.

(williams1992, 59) stated that professional writers, including academics and
journalists, seemed to be satisfied with the tools available in the early
1990s. They had adapted to these tools and did not seem to be aware of other
options. In the early 2000s, only writers who had used WordPerfect or other
word processors “back in the days” sometimes complained about missing
functionality in MS Word.

Today, users are willing to try out new interfaces and new writing
experiences. The implementation of applications with appealing user interfaces
is easier than ever: current programming languages and toolkits allow for fast
development and roll-out of responsive applications. At the same time, the
assumption that any writing tool must resemble MS Word is fading, which is
also driven by developments in creating, sharing, and accessing documents
beyond the paper-based structure (mahlow2022b, 35, 37).

For some time now, new writing applications as stand-alone tools or integrated
into other services—e.g., learning management systems or blogging software—are
being developed. As for the first wave of writing tools, we also see the
adoption of tools originally intended for writing code now for writing all
kinds of texts. The shift of academic writing to include dynamic aspects of
“text,” e.g., code (snippets), data plots, and other visualizations clearly
supports the use of these affordances.

##  3\. Where we should be going

The failed projects from the late 1980s addressed issues that can be
considered general considerations for the design and implementation of writing
technology:

  1. 1.

user-friendly interfaces, carefully designed functionality instead of ad-hoc
hacks (williams1990a, 58);

  2. 2.

support not only for writing, but also for teaching and learning how to write
(williams1989, 62), including sophisticated feedback on various levels to
stimulate reflection on the writing and decisions for revising and editing
(holt1990, 28);

  3. 3.

help for interpreting system messages and feedback (mcgowan1992, 40);

  4. 4.

easy extension of features based on user needs (holt1990, 28);

  5. 5.

real interaction with the system that enables writers to stay in control of
edits (dale1990a, 11);

  6. 6.

application of NLP technology users can trust (dale1996, 14);

  7. 7.

various views on the evolving text (rhetoric, linguistic, typographical,
graphemic) to stimulate creativity (kempen1986, 31).

Most of these requirements are generic requirements for software development
and emphasize the need for input from real users, both for functionality and
for the user interface. Strong collaboration between designers/developers of
writing tools and writing researchers modeling human writing processes at
multiple levels (e.g., the cognitive or the linguistic level) should be
established.

We are already seeing experimental applications that use recent technological
possibilities to finally approach writing in ways that previous experiments
could not realize: One such example is Tilio, which tried to implement the
ideas proposed by (sharples1999, 46) by understanding writing as design and
incorporating aspects and techniques today known as “design thinking.” While
this endeavor was halted by the COVID-19 situation in 2020, the technical
feasibility was demonstrated in an alpha version, so we may see another
attempt later. Similarly, the combination of different features and services
in an application like Scrivener for seamless integration of idea creation,
management of sources and references, connection to data tracing, and
communication channels (chat and messaging) can be seen as a functional
implementation of (engelbart1962, 19)’s ideas, even if the developers do not
explicitly refer to it.

In the late 1990s, projects like Intelligent Workstation, intended as an
instance of the “fifth generation of text-editing programs” (kempen1986, 31),
and Integrated Language Tools for Writing and Document Handling from KTH
Stockholm suffered from insufficient NLP resources. Today’s NLP tools make it
worth to reconsider the underlying ideas of those projects. They are also of
interest for document creation processes, as they already abstract from the
print-oriented document, which is in line with current developments: the
creation of texts for documents that can be rendered according to need and
display device.

The Web generally allows for _dynamic_ documents with respect to form and
content. Linking of documents as hypertext challenges authors during writing
but can be supported using recommender functionality based on artificial
intelligence (AI). The understanding of “text” changed at the turn of the
century to include “interactive, hypertextual documents—many of which reside
on the Internet—[which] use color, sound, images, video, words, and icons to
express their messages” (bazerman2001, 21, 282). This clearly requires tools
that allow writers to create and edit such documents; here again, writers
could be supported by powerful AI-based components.

Taking into account that communication takes place on various channels with
specific and complex formats emphasizes the need for structure within texts.
This allows the display of the content/text according to features of devices
and tailored to the needs of readers. Writing in these scenarios used to be
challenging and required knowledge of specific markup for rendering.
Abandoning WYSIWYG and its focus on printed paper documents, together with the
development of truly augmented and responsive writing tools based on
generative AI could actually free writers to “fully embracing the new
opportunities offered by digital media” (atzenbeck2021, 2).

(dale2021, 15) analyze the “automated writing assistance landscape in 2021.”
GPT-3 was already available at that time and was integrated into several tools
aimed at supporting writers as co-authors. These applications addressed
specific genres like blog posts and poetry, and specific writing tasks like
expanding, rewriting, and shortening texts (dale2021, 15). Some months later,
they were included as writing aids into experimental editors
[e.g.,][]dang2022,yuan2022. However, they were not widely used and did not
trigger the same discussions that we see now. We also see reimplementations of
popular applications with integrated access to LLMs. One such example is Lex,
intended as a “Google Docs style editor” (justin2022, 30). It has access to
GPT-3 and GPT-4 so that writers can invoke the language model to produce
plausible continuations of the text, taking into account everything before the
current cursor position, and to rewrite and summarize paragraphs. However,
research from a writing research perspective on how humans and AI-based
language models produce text through _co-creation_ is still pending at this
point.

In contrast to Dale’s 1997 prediction of augmented language-sensitive editors
within 10 years, Dale’s 2021 prediction seems feasible and even close to
reality, given the current pace of development in both machine learning-based
NLP and writing application implementations:

> But the big shift is the transition from tools that help with editing to
> tools that help with authoring. It’s conceivable that, in 5 years’ time, no
> automated writing assistance tool will be considered complete without a
> functionality that finishes your sentences, writes you out of tight corners
> and fills in background paragraphs while you doze at the keyboard. And given
> Microsoft’s exclusive licencing deal with OpenAI in regard to GPT-3, it
> won’t be a surprise if, before too long, we see some of these capabilities
> as yet another item on Microsoft Word’s ribbon menu. (dale2021, 15, 518)

Note that this prediction does not include the part of language-sensitive or
language-aware functionality supporting authors during production and revision
for semantic and pragmatic aspects.

##  4\. Conclusion

To design and implement writing tools effectively and efficiently, HCI
researchers must work closely with writing researchers to both foster the
development of operationalizable writing models and base the implementation of
writing tools on the latest insights into writing processes. Many ideas for
designing writing tools that actually address the needs of writers can be
gleaned from earlier projects by exploring the technical feasibility of the
underlying concepts. In this way, the development of writing tools would
finally respond to the demands and predictions made in the 1990s by (holt1992,
29) and (dale1997, 12).

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