Generative AI and Students Learning Experience

A conference paper that looks the impact of Generative AI on Students learning experience. Specifically, the presentation reviews the impact of Generative AI on higher education in terms of opportunity, challenges and the way forward for both instructors and school administrators

1. AI in Education; what happens to “Highered” in
5/10/15 years ahead?

2. Samuel Tweneboah-Koduah, Ph.D.
(Assistant Professor)
Computer and Cybersecurity
College of Engineering and Business
Gannon University, Erie, PA
GENERATIVE AI & STUDENTS
LEARNING EXPERIENCE
St. Louis, Missouri, USA
June 17 – 20, 2024

3. Outline
•Background
•Introduction
•The “Dilemma”
•Research Question
•Research Method
•Findings
•Analysis & Discussions
•Conclusion
•References
Generative AI and Students Learning
Experience

4. •Background
•Methodology
•Data Collection
•Results
•Analysis & Discussions
•Conclusion
•References
Generative AI and Students Learning
Experience

5. …the 6
th
wave: AI, Robots & Drones; what do we
know so far about AI in Education
Generative AI and Students Learning
Experience

6. Generative AI and Students Learning
Experience

7. Generative AI: What is it?
Multiple definitions; new ones still emerging….
The term Generative AI “refers to computational techniques that are capable
of generating seemingly new, meaningful content such as text, images, or
audio from training data”
Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond (2023).
Generative AI: an AI-based model that combines multiple AI algorithms (e.g.,
ML, DL, NLP, and LLMs) with trained data as an input to create new contents in
the form of text, images, audio and videos
Authors working definition (2024).

8. Generative AI: What is it?

9. Generative AI: How Does it work?

10. Generative AI: Use Cases
Image Synthesis and Editing: Generative AI can generate
realistic images based on given input or specific criteria.
Actual vs Deepfake

11. Generative AI: Use Cases
Text Generation (NLP) - :
Elicit: https://elicit.com/

12. Generative AI: Use Cases
Data Augmentation
✓Generative models can generate synthetic data to augment
existing datasets. This technique is particularly useful when
training machine learning models with limited labeled data, as it
helps improve model performance and generalization.
✓The technique is quite useful in biomedical research and studies
where empirical data is very difficult to come by

13. Use Case: Storytelling and Content Creation
✓Generative AI can generate storylines, plot twists, and character interactions,
aiding writers and storytellers in generating new narratives and content ideas
✓https://openai.com/index/government-of-iceland/

14. Generative AI: Others use cases include:
✓Music Composition
✓Video Game Design
✓Product Design and Prototyping
✓Video Synthesis and Deepfakes:
✓Medical Imaging and Drug Discovery
✓Fashion and Style Generation

15. AI in Education…Helpful or Harmful?

16. Generative AI in Education (Helpful)

17. Generative AI in Education:
Common Use Cases
i.Translation (global
office/international students
affairs)
ii.Idea Generation
iii.Content Creation
iv.Research & Analysis
v.Teaching Assistant
vi.Coding & Development
vii.Conversational AI (ChatBots)
viii.Records Management &
Archiving

18. AI in Education…the Good, the Bad and the Ugly

19. AI in Education…the Good, the Bad and the Ugly

20. AI in Education - The Dilemma….

21. The Dilemma (AI in Education]
https: //news. uark . edu/articles/69688/ai-outperforms-humans-in-standardized-tests-of-creativ e-potential
https://news.uark.edu/articles/69688/ai-
outperforms-humans-in-standardized-tests-of-
creative-potential

22. The Questions…
i.What are the Impacts of
Generative AI on Students’
Learning Experience?
ii.What are the challenges?
iii.What considerations are
needed for both Instructors
& School Administrators?

23. Questions-1: Impact of Generative AI on Learning Experience
i.Personalized learning
ii.Generate simulated environments for immersive learning experiences
and develop interactive educational content ((Wang et al., 2023;
Zhang, 2023; Baskara, 2023)
iii.Supporting students’ independent learning activities
iv.Adaptive testing,
v.Predictive analytics
vi.Chatbots are increasingly being utilized to deliver personalized
assistance and support to students’ engagement

24. Questions-1 (conti…): (Students) Learning Experience?
i.Intelligent tutoring (explaining concepts in multiple ways) (teaching
assistant)
ii.Real-time personalized feedback
iii.Idea generation and directions for conducting a science experiment
iv. Recommend learning content
v.Improve student engagement and interaction
vi.Facilitate group discussions and debates by providing a discussion structure
vii.Assist in skills development (reading, writing, math, science, and language)
viii.Code generation and explanation (for software development)
ix.Creating realistic virtual simulations for hands-on learning

25. Questions-1 (conti…)
(Instructors/Administrators Experience?
i.Personalized learning
ii.Syllabus design
iii.Creative lesson planning
iv.Curriculum development
v.Intelligent tutoring (i.e., creating or enhancing demonstrations/exampls)
vi.Creation of Homework, Quizzes, or Test Questions
vii.Monitor students’ learning progress (individualized solutions)
viii.Improve communication and its frequency between instructor and student
ix.Administrative tasks such as attendance checking, assignment correction
x.Responsive students’ engagement and feedback (i.e., chatbots)

26. •AI can automate administrative tasks, allowing educators to focus more
on individualized instruction and mentoring (Zhang, 2023; Wang et al.,
2023)
•AI can help automate the generation of educational materials, such as
textbooks or online resources, based on existing data (Pavlik, 2023)
•AI can improve accessibility and inclusivity in education by providing
adaptive and assistive technologies for students with disabilities (Wang et
al., 2023; Tanjga, 2023)
•Lastly, AI can also be used for assessment purposes. Automated
assessment systems powered by AI can analyze student responses and
provide immediate feedback, saving time for teachers and enabling timely
interventions to address learning gaps (Xu & Ouyang, 2022).
•Additionally, AI can facilitate the grading of open-ended and subjective
tasks, providing more accurate and consistent evaluations.
Questions-1 (conti…)
(Instructors/Administrators Experience)

27. Question 2: The Challenges?
i.Loss of creativity and novelty (missing human critical thinking but who cares?)
ii.False sense of knowledge acquisition and competencies (never mind all that
matters is the solution)
iii.Plagiarism (who owns what?): Major dilemma in students’ assessment (especially
for distance/online education)
iv.Ethical implications of using AI in education. AI systems, including chatbots and
generative AI, raise concerns about data privacy, algorithmic bias, and the
potential for automation to replace human educators (Kooli, 2023; Wang et al.,
2023; Tanjga, 2023)
v.Data Quality, & Security: AI use in students learning/research presents security
concerns in terms of data confidentiality and integrity. AI generated data (for
training) could be biased to satisfy the creator’s bid or ideology

28. i.Dataset Bias and Generalization: Generative models heavily rely on training
datasets they are exposed to. If the training data is biased or limited, the
generated outputs may inherit those biases or struggle with generalizing to
unseen scenarios.
ii.Computational Resources and Complexity: Training and deploying generative
models can be computationally intensive and require significant resources,
including highly skilled personnel, high-performance hardware and substantial
training times.
iii.Quality and Coherence: While generative models have made significant
progress, they may still struggle with producing outputs that consistently
exhibit high quality, coherence, and contextual relevance.
Question 2: The Challenges (conti…):?

29. Question 2: (Institutional Concerns –
myth or reality?)
✓Lack of institute-wide AI policy
✓Universities mostly don’t have AI Governance
✓Where do we start from when it comes to Generative AI
✓Tools to detect AI generated contents? (major issue for academic dishonesty). Are
current tools accessible, affordable and reliable?
✓The “Legal Hurdle” we all want to avoid…..(what are the current laws saying, when
it comes to AI in education and the regulators?)

30. Questions-3: The way forward – Requirements & Regulations
i.Assessments: Total overhaul of Test-based assessments. Instructors
must also review take-home projects as well as class assessments in
the era of the AI era
ii.Administrative Oversight (Top-down approach required)
iii.Plagiarism Prevention Applications (Acquisition: develop, lease or
outsource?)
iv.Setting Clear AI-Use guidelines (Accept and champion the change)
v.Alternative curriculum to improve students critical thinking skills
vi.Problem-based learning (the need for new/reviewing curriculums)
vii.Ethical guidance (new AI-in-Education policy)
viii.Social good vs social harm (requires collective engagement at all
levels)
ix.Data Privacy and protection guidelines (Institutions need clear policy
on AI generated, use, processing, storage, and disposition of
educational data)

31. Questions-3: The way forward – Requirements & Regulations
i.Assessments: Total
overhaul of Test-based
assessments. Instructors
must also review take-
home projects as well as
class assessments in the
era of the AI era
ii.Administrative Oversight
(Top-down approach
required)
iii.Plagiarism Prevention
Applications (Acquisition:
develop, lease or
outsource?)

32. Generative AI can be completely wrong when we set a
technical and critical thinking types of question. Example…

33. Solution from Generative AI
https://chatgpt.com/

34. The right solution…..
Conclusion:
Students can be forced to think beyond the Box of AI, if instructors can set some
technical and critical thinking questions

35. Research Methods
Data collection
➢Scholarly Literature acquired through Google Scholar, IEEE, ACM,
Scopus, Sciencedirect, of keyword phrases
➢English language scholarly papers from 2021 onward were utilized
➢Only Higher Education, no high school, no elementary, nor trade
schools were included
Scope
➢Started with gathering papers on the topic utilizing keyword phrases
➢Evaluating papers for their quality
➢Rating the papers for their relevance toward the objectives
➢Results: NVivo keyword phrases
➢Analysis: Theming from Keywords
➢Discussion putting those together in Tree Maps for easy
dissemination
➢Summarizing collective work in conclusion

36. Reference (selected few)
i.F. M. Megahed, Y.-J. Chen, J. A. Ferris, S. Knoth, and L. A. Jones-Farmer, “How generative AI models such as ChatGPT can be (mis)used in
SPC practice, education, and research? An exploratory study,” Qual. Eng., p. 30, Jun. 2023, doi: 10.1080/08982112.2023.2206479.
ii.P. Perera and M. Lankathilaka, “AI in Higher Education: A Literature Review of ChatGPT and Guidelines for Responsible Implementation,”
Int. J. Res. Innov. Soc. Sci. IJRISS, vol. 7, no. 6, pp. 306–314, 2023.
iii.J. Qadir, “Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education,” in 2023 IEEE Global
Engineering Education Conference (EDUCON), May 2023, pp. 1–9. doi: 10.1109/EDUCON54358.2023.10125121.
iv.E. Tajik and F. Tajik, “A comprehensive Examination of the potential application of Chat GPT in Higher Education Institutions,” TechRxiv
Prepr., pp. 1–10, 2023.
v.M. A. Peters et al., “AI and the future of humanity: ChatGPT-4, philosophy and education – Critical responses,” Educ. Philos. Theory, pp. 1–35,
Jun. 2023, doi: 10.1080/00131857.2023.2213437.
vi.O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher
education – where are the educators?,” Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, p. 27, Dec. 2019, doi: 10.1186/s41239-019-0171-0.
vii.S. Ali, D. DiPaola, I. Lee, J. Hong, and C. Breazeal, “Exploring Generative Models with Middle School Students,” in Proceedings of the 2021
CHI Conference on Human Factors in Computing Systems, Yokohama Japan: ACM, May 2021, pp. 1–13. doi: 10.1145/3411764.3445226
viii.S. Grassini, “Shaping the future of education: exploring the potential and consequences of AI and ChatGPT in educational settings,” Educ. Sci.,
vol. 13, no. 7, p. 13, 2023.
ix.S. S. Gill et al., “Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots,” Internet Things Cyber-Phys. Syst.,
vol. 4, p. 5, 2024.
x.“NVivo,” Lumivero. Accessed: Dec. 06, 2023. [Online]. Available: https://lumivero.com/products/nvivo/

37. Collaborative Authors
Kalivoda, Marcus P
Erie Fire Service Department
Computer and Information Systems
Gannon University, Erie, PA

Nyantakyi, Kwadwo, Ph.D.
GIMPA Business School, Greenhill College,
Ghana Institute of Management and Public
Administration, Accra, Ghana

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