University of Calgary PRISM: University of Calgary's Digital Repository

Werklund School of Education Werklund School of Education Research & Publications

2021-06-01 Artificial , Algorithmic Writing & Educational Ethics

Eaton, Sarah Elaine; Mindzak, Michael; Morrison, Ryan

Eaton, S.E., Mindzak, M., & Morrison, R. (2021, May 29 - June 3). , Algorithmic Writing & Educational Ethics [Paper presentation]. Canadian Society for the Study of Education Société canadienne pour l'étude de l'éducation, Edmonton, AB, Canada. http://hdl.handle.net/1880/113569 conference paper

Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca THE IMPACTS OF TEXT GENERATING TECHNOLOGIES ON ACADEMIC INTEGRITY: AI & AI CHALLENGES AND OPPORTUNITIES POSED BY GPT-3 AND OTHER TEXT GENERATING ALGORITHMS.

Dr. Sarah Elaine Eaton University of Calgary [email protected] Dr. Michael Mindzak Brock University [email protected] Ryan Morrison –M. Ed (IT) George Brown College [email protected] AGENDA • Introductions • Turing Tests • OpenAI's GPT Development • Academic Integrity • Theoretical Considerations • Future Directions • Conclusion LET’S START WITH A QUICK TURING TEST:

• Be the judge -- what is the product of a human and what is the product of a machine?

• Identical input (Prompts)

• Which one of the two texts in front of you was written by a computer, and which one by a human? PROMPT: YOU ARE LOST IN THE FOREST TEXT A: Lost in the forest, I become convinced that TEXT B: You are lost in the forest and you got lost something is stalking me. I hear a rustling behind me, and because you did not listen to the trees .You are not when the wind blows through the leaves on the trees, my the wood and you are not the tree, it is the wood sense is that there is something breathing beneath the and the tree that allow you to be lost in the forest. whisper of the breeze. It’s definitely not human, but it You cannot survive if you continue to resist in the doesn’t seem quite animal either. It’s more like something forest. You should just stay there with a little smile on that’s part of the life of the forest—separate from any your face that you are just the blackwood. It is the particular burst of weather, more a part of the ancient thick tree which allowed you to be here as long as it ecosystem of the forest itself. But, beyond this, is the is thick ,and not the little weak tree which is not able certainty that, whatever it is – however this unseen to provide you with enough strength to survive in the element force might be embodied – it is malevolent. It forest. So we shouldn’t be focused on the tree and does not wish me well. It does not want me here. I found the woods because the tree provides us with no my way into the forest, and now I am lost in the future. And you should try not to feel all alone and forest. Now I am certain that I will never leave. always stay focused on the forest and our plans. • HUMAN, 50 • GPT-3 • NATIVE ENGLISH SPEAKER • INFERKIT, DEFAULT SETTINGS • MICROSOFT WORD • 3 TRIES - 30 SECONDS TO • 4.5 MINUTES TO COMPLETION COMPLETION TIMELINE OF OPENAI’S DEVELOPMENT OF GENERATIVE PRE-TRAINED TRANSFORMER (GPT)

Winter 2020: GPT-3 platforms are November 2019: available to the public. Microsoft GPT-2 released to begins integrating GPT-3 predictive public through text into its Editor function in its several platforms. Office365 suite.

February 2019: Fall 2020: Limited release of Spring 2021: GPT-3 revealed to GPT-2 is first GPT-3. The Guardian’s article be able to create images, code exhibited by “A Robot Wrote this Article: and text in several languages. OpenAI, but not Are you scared yet human?” Platforms have begun integrating available to the goes viral. Other examples of purpose-built functions. Free general public GPT-3 text passing as human version announced: GPT-NEO text happens on Reddit and Hacker News. HOW BIG ARE THE DATA SETS?

This is from a set very similar to GPT-3 from Eleuther AI, called "The Pile"

It's used to train language models and is 800 GB in size

4 categories of text from open sources: Academic, Internet, Dialogue, Prose, Dialogue and Miscellaneous IN THE ROBOT- NEWS.....

STRANGELY WORDED SPAM... SOME PLATFORMS TO EXPLORE

HTTPS://INFERKIT.COM/ HTTPS://WWW.COPY.AI/ HTTPS:/HEADLIME.COM/ “MORE THAN 300 APPLICATIONS ARE NOW USING GPT-3, AND TENS OF THOUSANDS OF DEVELOPERS AROUND THE GLOBE ARE BUILDING ON OUR PLATFORM. WE CURRENTLY GENERATE AN AVERAGE OF 4.5 BILLION WORDS PER DAY, AND CONTINUE TO SCALE PRODUCTION TRAFFIC.” – OPENAI BLOG POST, MARCH 25, 2021 A list of the types of text GPT-3 has been trained to create on copy.ai WHAT ARE ITS STRENGTHS?

• GPT-3 is OPEN -- anyone can access the algorithm and use it for development. • Speed and scale – generates very quickly and can be adjusted for specific tasks. • Grammar is largely accurate – only discrete errors. • Provides insights through volume and immediacy of text – great for getting over writer's block. • Every text generated is original and will disappear from existence if not used – "Turn It In" and "Safe Assign" will not detect it. WHAT ARE ITS WEAKNESSES AS FAR AS WRITING? • GPT-3 makes up facts and stories to fit the prompt • It misattributes quotes and has no function to verify its “research” • If left unchecked, it strays from the topic at hand. Rarely does it return to the thesis/ prompt if left to 'run'. • It uses lots empty phrases seemingly to expand word count – Despite having one "That's a good question" of the best developing financial • It makes occasional logical errors and contradicts itself. systems in standardized form, • Word form issues specifically with adjectives and adverbs our youth are more financial illiterate • In short, it is currently a ~C student for academic purposes than most PRELIMINARY INVESTIGATION CONCURS....

• College professors blind graded GPT-3 written assignments alongside student work.

• GPT-3 was able to achieve C+ / B- grades on academic essays.

• GPT-3 required significant prompting to produce a lengthy creative narrative, and still was only able to achieve a D- Literature Review - Academic Integrity • Academic integrity scholars often view plagiarism primarily as a writing development issue (e.g., Howard 1992, 1995, 2000; Panning Davies et al., 2016; Robillard & Howard, 2008; Robillard, 2008) • Authorial agency and identity factor into academic integrity (Howard, 1995; Sutherland-Smith, 2008; Thompson, 2005). • Paraphrasing and text-spinning technologies have become part of the discussion around academic integrity over the past decade (Elmes, 2017; Prentice & Kinden, 2018; Rogerson & McCarthy, 2017) Algorithmic Writing & Academic Integrity Students

• As GPT-3 continues to develop, student will likely be at the vanguard • Students will/are using technological tools to enhance their academic writing • Students will continue to push the boundaries of academic integrity • I.e "what are the chances of detection? • While writing practices and norms differ between subject areas, we can assume that students across all disciples will utilize text-generators. CASE STUDY

Mike is bright young computer programmer studying but has chosen to take some electives in History. Noticing that his papers have received low grades, he decides to utilize his skills by downloading the GPT-3 source code and programming a model himself-- which produces some very submittable papers. However, he wonders if this is plagiarism, as he has not written a single word for the paper itself, but he did write the code and develop the algorithm all by himself.... ALGORITHMIC WRITING & ACADEMIC INTEGRITY STUDENTS

• Students will continue to use text-generators so long as they find them useful and productive • For-Profit "ghost writing" will proliferate as text-generators will allow them to provide their services more efficiently • Students will use text-generators in various ways and thus, some may not view these actions as "cheating" • Unless academic integrity policies are explicit and enforceable, students will likely continue down this path Algorithmic Writing & Academic Integrity Educators • Educators will likely continue to be reactive rather than proactive as GPT-3 technologies evolve rapidly

• Today, many educators rely on similar technologies for plagiarism detection (while are also improving rapidly)

• Educators in certain fields may be more/less concerned than others concerning academic writing integrity

• Educators will likely need institutional guidance on how to best approach these issues (policy uniformly or not) CASE STUDY

Mike is an average student, but an extremely critical and divergent thinker. He uses a text- generator to help him complete his 12-page paper on Applied Ethics. He cites GPT-3 and explains in the footnotes that the machine helped write about 49% of the paper (which he edited) and he did the rest himself. He further argues that this is an original paper. His instructor finds the paper insightful and provocative but is unsure how it should be evaluated... ALGORITHMIC WRITING & ACADEMIC INTEGRITY EDUCATORS • In the short-term, educators will likely be unaware or else unsure of how to approach text-generators and plagiarism • Higher education could find itself forced to return to more "analog" solutions • Ex- pencil & paper • Other educators may instead choose to integrate these technologies into their teaching and writing practices • Ex- automated grading, tighter scaffolding • Overall, educators will have to shift their values concerning the assessment and evaluation of academic writing Algorithmic Writing & Academic Integrity Researchers

• Researchers in higher educator will also be implicated in the development of algorithmic writing

• This had already occurred back in 2005 by a group of MIT researchers

• As with students, these developments may occur unevenly due to norms across disciplines

• Text-generators have the potential to both positively and negatively impact academic research and scholarship CASE STUDY Mike is a relatively bright and intrepid scholar who hopes to one day secure a tenure track position. He realizes one of the best ways to do this is to publish as many possible articles, essays and books as he can. He has a programmer create a custom GPT-3 tool which focuses on Educational Leadership literature and automates his writing. Many of these manuscripts pass peer review. As his CV grows with more and more publications, he begins to wonder if this behavior is really ethical and what his colleagues (or a hiring committee) would think if they found out...... ALGORITHMIC WRITING & ACADEMIC INTEGRITY RESEARCHERS • As a researcher, would you use technology tools which would assist your research and writing? (hint- you already probably are!) • As things stand, it appears as though many researchers would be incentivized to use text-generators in the research process (+/-) • GPT-3 tools may be used on other ways such as in generating literature reviews, form filling or automating the peer-review process • Concerns over republication and sharing of work (open-access) • This may also ask further questions regarding hiring and promotion with academia • Continued questioning or resistance to current neoliberal publish or perish paradigms AI & ACADEMIC ETHICS

• Definitions of Academic Integrity & Plagiarism • Student Writing Evaluation & Assessment • Teaching & Pedagogical Writing Practices • Research Ethics • Privacy Concerns

Text-Generators such as GPT-3 are salient examples of broader discussions and debates concerning the emergent relationship between Artificial Intelligence and Higher Education AI, AUTHORSHIP & ORIGINALITY

• Copyright & Intellectual Property • Ethical & Legal Dimensions • The "Automation of Authorship" • Can everyone be a "good author"? • Evolving "Creativity" • Ex- DALL-E • Reconceptualizing Originality • AI + Humans as "Co-creators" • Quality/Quantity of Writing • Time, Efficiency & Incentives LOOKING FORWARD: THOUGHTS AND PROGNOSTICATIONS

RYAN MIKE QUESTIONS?

COMMENTS?

CONCERNS? REFERENCES Artificial Intelligence: The Turing Test. (1999). Retrieved from http://www2.psych.utoronto.ca/users/reingold/courses/ai/turing.html

GLTR from MIT-IBM AI Lab and Harvard NLP. (2019). Retrieved from http://gltr.io/dist/index.html

Guo, L.., Biderman, S., Black, S. Golding, L. Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S. & Leahy, C (2020, December 31). The Pile: an 800GB Dataset of Diverse TexT for Language Modeling. EleutherAI. . https://arxiv.org/pdf/2101.00027.pdf

King, A. (2019, November 5) Talk to Transformer. Retrieved from https://talktotransformer.com/

Knight, W. (2019, July 26). A new tool uses AI to spot text written by AI. Technology Review. Retrieved from https://www.technologyreview.com/f/614021/a-new-tool-uses-ai-to-spot-text-written-by-ai/

Lyons, K. (2020, August 16). A college student used GPT-3 to write fake blog posts and ended up at the top of Hacker News. The Verge. Retrieved November 15, 2020, from https://www.theverge.com/2020/8/16/21371049/gpt3-hacker- news-ai-blog

Macaulay, T. (2020, October 07). Someone let a GPT-3 bot loose on Reddit - it didn't end well. The Next Web. Retrieved November 15, 2020, from https://thenextweb.com/neural/2020/10/07/someone-let-a-gpt-3-bot-loose-on-reddit-it-didnt- end-well/ REFERENCES Macaulay, T. (2021, February 18). Who writes better essays? College students of GPT-3. The Next Web. Retrieved April 9, 2021, from https://thenextweb.com/neural/2021/02/18/gpt3-ai-college-essay-grades-compared-students/

Mindzak, M. (2020). What happens when a machine can write as well as an academic? https://www.universityaffairs.ca/opinion/in-my- opinion/what-happens-when-a-machine-can-write-as-well-as-an-academic/

OpenAI. (2020). OpenAI Licenses GPT-3 Technology to Microsoft. OpenAI Blog. Retrieved November 15, 2020 from https://openai.com/blog/openai-licenses-gpt-3-technology-to-microsoft/

Porr, L. & GPT-3. (2020, September 08). A robot wrote this entire article. Are you scared yet, human? The Guardian. Retrieved November 15, 2020, from https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3

Radford, A., Wu, J., Amodei, D., Amodei, D., Clark, J., BrundageIlya, M. & Sutskever, I. (2019, February 14). Better Language Models and Their Implications. OpenAI. Retrieved from https://openai.com/blog/better-language-models/

Solaiman, I., Clark, J. & Brundage, M. (2019, November 5) GPT-2: 1.5B Release. OpenAI Retrieved from https://openai.com/blog/gpt-2-1- 5b-release/

Write With Transformer. (2019, November). Retrieved from https://transformer.huggingface.co/doc/gpt2-xl

This Is the Most Powerful Artificial Intelligence Tool in the World https://www.entrepreneur.com/article/368843