Slide deck for teachers and policy makers on opportunities and issues of ChatGPT for student writing.

1. Mike Sharples
Institute of Educational Technology
The Open University
www.mikesharples.org
GPT and Student Writing
@sharplm

2. A critique of learning styles
The construct of "learning styles" is problematic because it oversimplifies the complex process of learning and has not been supported by empirical evidence. Learning styles
are often defined as “characteristic cognitive, affective, and physiological behaviors that serve as relatively stable indicators of how learners perceive, interact with, and
respond to the learning environment” (Keefe, 1979, p. 37). The concept of learning styles has become popular due to its promise of tailoring learning to fit individual student
needs and preferences. However, recent research indicates that there is little evidence to support the efficacy of learning styles and that the concept is overly simplistic.
The idea of learning styles is based on the notion that individuals have distinct preferences for how they learn and process information. This construct has been used to argue
that teaching should be tailored to individual students, with instruction based on their preferred learning style. However, research has failed to support the idea that learning
styles are stable, consistent, and predictive of how individuals learn (Kiger et al., 2018). For example, a meta-analysis by Pashler et al. (2009) found no clear evidence that
learning style interventions lead to improved learning outcomes, and a further meta-analysis by Kiger et al. (2018) found no reliable link between learning style preferences
and performance.
In addition, the notion of learning styles is overly simplistic and fails to account for the complexity of learning. Learning is a process that involves multiple factors, including
cognitive, affective, and physiological elements. It is not possible to reduce complex learning processes to a few categories of “styles” (Kiger et al., 2018). Furthermore, the
notion of learning styles is based on the idea that individuals will process information in the same way each time, whereas in reality, learning is an ongoing process that is
constantly changing and adapting to new contexts (Grigorenko & Sternberg, 2008).
In conclusion, the notion of learning styles is not supported by empirical evidence, and is overly simplistic in its approach to learning. Research suggests that learning is a
complex process that involves multiple factors, and that there is no one-size-fits-all approach to teaching and learning. Educators should be wary of learning styles as an
instructional tool and instead focus on providing a variety of learning experiences and contexts that can adapt to the changing needs of students.
References
Grigorenko, E.L., & Sternberg, R.J. (2008). Dynamic testing: The nature and measurement of learning potential. New York, NY: Cambridge University Press.
Keefe, J.W. (1979). Learning styles: Implications for improving educational practices. Review of Educational Research, 49(3), pp. 375-409.
Kiger, G., Lomax, R., & Ross, R. (2018). Learning styles: A comprehensive review of the literature. Educational Research Review, 24, pp. 1-36.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), pp. 105-119.

3. That “student
essay” was
entirely generated
by an AI program:
GPT-3, from
OpenAI

4. GPT-3
Highly-trained text completer and
style copier
Attends to the previous 750 words
and can generate up to 3000 words
It can write in any style in multiple
languages
It can be given a direct instruction
It can summarise a scientific article in
simpler language, write a review,
translate languages, and answer
questions.
It is a general-purpose language tool

5. ChatGPT – a
conversational
agent based on
GPT-3
https://openai.com/blog/chatgpt/
Part of my dialogue with ChatGPT about plagiarism

6. https://stanforddaily.com/2023/01/22/scores-of-stanford-
students-used-chatgpt-on-final-exams-survey-suggests/
Students are
already using GPT-
3 and ChatGPT for
exams
AI is democratising
cheating

7. Plagiarism detectors don’t work – the
text is generated, not copied
AI detectors have low reliability
OpenAI’s detector tool labels 9% of
human-written text as written by AI
New Turnitin AI detector
Claims of less than 1 in 100 false
positives need to be independently
verified

8. A critique of learning styles
The construct of "learning styles" is problematic because it oversimplifies the complex process of learning and has not been supported by empirical evidence. Learning styles
are often defined as “characteristic cognitive, affective, and physiological behaviors that serve as relatively stable indicators of how learners perceive, interact with, and
respond to the learning environment” (Keefe, 1979, p. 37). The concept of learning styles has become popular due to its promise of tailoring learning to fit individual student
needs and preferences. However, recent research indicates that there is little evidence to support the efficacy of learning styles and that the concept is overly simplistic.
The idea of learning styles is based on the notion that individuals have distinct preferences for how they learn and process information. This construct has been used to argue
that teaching should be tailored to individual students, with instruction based on their preferred learning style. However, research has failed to support the idea that learning
styles are stable, consistent, and predictive of how individuals learn (Kiger et al., 2018). For example, a meta-analysis by Pashler et al. (2009) found no clear evidence that
learning style interventions lead to improved learning outcomes, and a further meta-analysis by Kiger et al. (2018) found no reliable link between learning style preferences
and performance.
In addition, the notion of learning styles is overly simplistic and fails to account for the complexity of learning. Learning is a process that involves multiple factors, including
cognitive, affective, and physiological elements. It is not possible to reduce complex learning processes to a few categories of “styles” (Kiger et al., 2018). Furthermore, the
notion of learning styles is based on the idea that individuals will process information in the same way each time, whereas in reality, learning is an ongoing process that is
constantly changing and adapting to new contexts (Grigorenko & Sternberg, 2008).
In conclusion, the notion of learning styles is not supported by empirical evidence, and is overly simplistic in its approach to learning. Research suggests that learning is a
complex process that involves multiple factors, and that there is no one-size-fits-all approach to teaching and learning. Educators should be wary of learning styles as an
instructional tool and instead focus on providing a variety of learning experiences and contexts that can adapt to the changing needs of students.
References
Grigorenko, E.L., & Sternberg, R.J. (2008). Dynamic testing: The nature and measurement of learning potential. New York, NY: Cambridge University Press.
Keefe, J.W. (1979). Learning styles: Implications for improving educational practices. Review of Educational Research, 49(3), pp. 375-409.
Kiger, G., Lomax, R., & Ross, R. (2018). Learning styles: A comprehensive review of the literature. Educational Research Review, 24, pp. 1-36.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), pp. 105-119.

9. Introduction
Headings &
paragraphs
References
Conclusion
Progression
Examples
Flawless
grammar
Citations
A critique of learning styles
The construct of "learning styles" is problematic because it oversimplifies the complex process of learning and has not been supported by empirical evidence.
Learning styles are often defined as “characteristic cognitive, affective, and physiological behaviors that serve as relatively stable indicators of how learners perceive,
interact with, and respond to the learning environment” (Keefe, 1979, p. 37). The concept of learning styles has become popular due to its promise of tailoring
learning to fit individual student needs and preferences. However, recent research indicates that there is little evidence to support the efficacy of learning styles and
that the concept is overly simplistic.
The idea of learning styles is based on the notion that individuals have distinct preferences for how they learn and process information. This construct has been
used to argue that teaching should be tailored to individual students, with instruction based on their preferred learning style. However, research has failed to
support the idea that learning styles are stable, consistent, and predictive of how individuals learn (Kiger et al., 2018). For example, a meta-analysis by Pashler et al.
(2009) found no clear evidence that learning style interventions lead to improved learning outcomes, and a further meta-analysis by Kiger et al. (2018) found no
reliable link between learning style preferences and performance.
In addition, the notion of learning styles is overly simplistic and fails to account for the complexity of learning. Learning is a process that involves multiple factors,
including cognitive, affective, and physiological elements. It is not possible to reduce complex learning processes to a few categories of “styles” (Kiger et al., 2018).
Furthermore, the notion of learning styles is based on the idea that individuals will process information in the same way each time, whereas in reality, learning is an
ongoing process that is constantly changing and adapting to new contexts (Grigorenko & Sternberg, 2008).
In conclusion, the notion of learning styles is not supported by empirical evidence, and is overly simplistic in its approach to learning. Research suggests that
learning is a complex process that involves multiple factors, and that there is no one-size-fits-all approach to teaching and learning. Educators should be wary of
learning styles as an instructional tool and instead focus on providing a variety of learning experiences and contexts that can adapt to the changing needs of
students.
References
Grigorenko, E.L., & Sternberg, R.J. (2008). Dynamic testing: The nature and measurement of learning potential. New York, NY: Cambridge University Press.
Keefe, J.W. (1979). Learning styles: Implications for improving educational practices. Review of Educational Research, 49(3), pp. 375-409.
Kiger, G., Lomax, R., & Ross, R. (2018). Learning styles: A comprehensive review of the literature. Educational Research Review, 24, pp. 1-36.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), pp. 105-119.

10. A critique of learning styles
The construct of "learning styles" is problematic because it oversimplifies the complex process of learning and has not been supported by empirical evidence.
Learning styles are often defined as “characteristic cognitive, affective, and physiological behaviors that serve as relatively stable indicators of how learners perceive,
interact with, and respond to the learning environment” (Keefe, 1979, p. 37). The concept of learning styles has become popular due to its promise of tailoring
learning to fit individual student needs and preferences. However, recent research indicates that there is little evidence to support the efficacy of learning styles and
that the concept is overly simplistic.
The idea of learning styles is based on the notion that individuals have distinct preferences for how they learn and process information. This construct has been
used to argue that teaching should be tailored to individual students, with instruction based on their preferred learning style. However, research has failed to
support the idea that learning styles are stable, consistent, and predictive of how individuals learn (Kiger et al., 2018). For example, a meta-analysis by Pashler et al.
(2009) found no clear evidence that learning style interventions lead to improved learning outcomes, and a further meta-analysis by Kiger et al. (2018) found no
reliable link between learning style preferences and performance.
In addition, the notion of learning styles is overly simplistic and fails to account for the complexity of learning. Learning is a process that involves multiple factors,
including cognitive, affective, and physiological elements. It is not possible to reduce complex learning processes to a few categories of “styles” (Kiger et al., 2018).
Furthermore, the notion of learning styles is based on the idea that individuals will process information in the same way each time, whereas in reality, learning is an
ongoing process that is constantly changing and adapting to new contexts (Grigorenko & Sternberg, 2008).
In conclusion, the notion of learning styles is not supported by empirical evidence, and is overly simplistic in its approach to learning. Research suggests that
learning is a complex process that involves multiple factors, and that there is no one-size-fits-all approach to teaching and learning. Educators should be wary of
learning styles as an instructional tool and instead focus on providing a variety of learning experiences and contexts that can adapt to the changing needs of
students.
References
Grigorenko, E.L., & Sternberg, R.J. (2008). Dynamic testing: The nature and measurement of learning potential. New York, NY: Cambridge University Press.
Keefe, J.W. (1979). Learning styles: Implications for improving educational practices. Review of Educational Research, 49(3), pp. 375-409.
Kiger, G., Lomax, R., & Ross, R. (2018). Learning styles: A comprehensive review of the literature. Educational Research Review, 24, pp. 1-36.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), pp. 105-119.
Invented
research
study
Fake
academic
paper

11. Generative AI
hallucinates
It doesn’t “know” it shouldn’t
invent research studies,
it has no explicit model of how the
world works,
can’t access current information,
and is amoral.
It is a language model, not a
database or reasoning system https://openai.com/blog/instruction-
following/

12. Ban
Confident students will continue to
use AI, and will challenge decisions
based on AI detectors.
Evade
Invigilated exams are costly and
limited.
Adapt
Requires new methods of
assessment, new policies and
guidelines

13. Generative AI can
be an
empowering, joyful
tool for creativity

14. Educator or student uses
AI to generate multiple
responses to an open
question. Each student
synthesises and critiques
the AI responses, to create
their own written answer.
Assessment for learning with generative
AI

15. Educator sets a project for
students. Each student uses
generative AI as a tool for
research. Student writes a
project report, indicating the
contribution of AI.
Assessment for learning with generative
AI

16. In an individual or group
activity, students engage with
ChatGPT in a Socratic
dialogue, then each student
writes an argumentative essay.
Assessment for learning with generative
AI

17. Use generative AI with care
Rethink written assessment
Explore AI for creativity,
argumentation and
research
Develop AI literacy
Introduce and negotiate
guidelines for students and
staff

18. Guidelines for students on best practices for using generative AI systems in
education
1. Understand the limitations of the AI system: Generative AI systems are language models, not
knowledge bases. They may not produce accurate or reliable information and should not be used as a
sole source for research or assignments.
2. Use the AI system as a supplement: Use the AI system to supplement, not replace, your own critical
thinking and research. Verify the information generated by the AI system with other sources before
using it in your work.
3. Cite your sources: If you use information generated by the AI system in your work, be sure to cite it
as a source. Plagiarism is a serious issue and academic integrity is of the utmost importance.
4. Be aware of ethical concerns: Be aware of the ethical concerns surrounding the use of AI systems
and strive to use them responsibly and in an ethical manner.
5. Communicate with your instructors: If you are unsure about how to use the AI system or have
concerns, talk to your instructors. They can provide guidance and advice on how to use the AI system
effectively.
6. Continuously evaluate and improve: Continuously evaluate the effectiveness of using AI systems in
your education and adapt accordingly. Provide feedback to improve the AI system for future use.
7. Embrace the opportunity to learn: Use AI systems as an opportunity to learn more about technology,
AI and its impact on society and the future.
Guidelines generated by ChatGPT, 28.1.2023

19. What next?
AI-generated video blogs
and courses
Topical generative AI
that reports on current events and its online
experience
Common-sense generative
AI
that reasons about the world
Interactive storyworlds
https://youtu.be/FMipOk7jPCI

20. Describes
machines as
authors of fiction:
past, present and
future.
Explores what it
means to be a
creative writer, and
whether humans
are “story