The Current State of AI in Teaching and Learning in Higher Education

Prof. dr. sc. Markus Schatten

Head of Artificial Intelligence Laboratory

Head of doctoral study programme Information Science

Faculty of Organization and Informatics

University of Zagreb

markus.schatten@foi.hr

Zagreb, 23.10.2024.

Introduction

AI is transforming teaching and learning in higher education.
Today, we'll focus on policies, tools, and future directions.
Let's explore where we are now and what's ahead.

AI Policies in Higher Education

Guidelines: EC AI Act (2021), EP Committee on Culture and Education Report on artiﬁcial intelligence in education, culture and the audiovisual sector, EUHLEX on AI Ethics Guidelines for Trustworthy AI (2019), UNESCO Recommendation on the Ethics of AI (2022), OECD Recommendation of the Council on AI (2019)
Growing need for AI policies in universities.
Policies vary widely across institutions.
Focus on privacy, academic integrity, and data ethics.

Challenges in AI Policy Development

Some universities have introduced AI guidelines early.
Many universities lack comprehensive AI guidelines.
Ethical concerns: accountability, transparency, human oversight, privacy.
Risks of inconsistent AI policy enforcement.

Recommendations for Cohesive AI Policy Frameworks

Create standardized policies to address ethical AI usage.
Ensure transparency in how data is processed and used.
Promote AI literacy among faculty and students.
Focus on student responsibility and transparency in using AI.

AI Tools in Teaching and Learning

AI tools are reshaping both student learning and faculty teaching methods.
Adoption challenges: faculty readiness, AI literacy.

https://www.futuretools.io/

Intelligent Tutoring Systems and Adaptive Learning

Personalized learning through adaptive AI platforms.
Systems tailor content to individual student needs.
Possible improved engagement and knowledge retention.

Enhanced Learning Analytics

AI can provide detailed analysis about student engagement.
Personalized assessments based on student performance.
Identify possible course failures, dropouts but also talented students early.

Virtual Assistants and AI-Powered Chatbots

AI-driven chatbots answer student queries 24/7.
Enhance communication and streamline administrative tasks.
Free up educators' time for more meaningful interactions.

AI in Content Creation and Resource Recommendation

AI assists educators in generating quizzes, summaries, and materials.
Provides personalized recommendations for student resources.

Challenges and Concerns in AI Tool Adoption

Data privacy and algorithmic bias are significant concerns.
Dehumanization of education.
Faculty readiness and AI literacy remain challenges.

The Future of AI in Higher Education

AI will continue to shape learning outcomes and teaching models.
Institutions must prepare students for AI-driven workplaces.
Focus on interdisciplinary skills and AI literacy in the future curriculum.

AI and the Transformation of Learning Outcomes

AI literacy and problem-solving as key future outcomes.
Move from traditional exams to competency-based, project-based and task-based assessments.
Shift to collaborative, interdisciplinary learning.
Preparing students for real-world applications of AI.

Future Research Directions

Future research on AI competencies and interdisciplinary teaching.
Investigate innovative pedagogies and AI-driven assessment strategies.
Universities must lead research in evolving AI education trends.

Conclusion and Future Vision

AI offers both opportunities and challenges for higher education.
Addressing policy gaps and promoting AI literacy is essential.
The future of AI in education can be inclusive, effective, and ethical.

Bibliography

Dabis, A., & Csáki, C. (2024). AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI.
Nikolopoulou, K. (2024). Generative Artificial Intelligence in Higher Education: Exploring Ways of Harnessing Pedagogical Practices with the Assistance of ChatGPT.
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives.
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI.

Thank you!

Questions? Comments? Suggestions? Critique?

contact: markus.schatten@foi.unizg.hr

Disclaimer: This presentation was constructed with the help of AI.