Track 3: Ethical, Cognitive, and Pedagogical Implications of Generative AI in Higher Education

Organizers

Track Chair: Dr. Mei Mei Lau

Senior Lecturer
Division of Business and Hospitality Management (BHM), College of Professional and Continuing Education (CPCE), The Hong Kong Polytechnic University
E-mail: may.lau@cpce-polyu.edu.hk
Personal Website: https://profile.cpce-polyu.edu.hk/en/persons/mei-mei-may-lau

Co-Chairs
Dr. Aris Yuk Chau Lam
Associate Division Head, Senior Lecturer, Division of Business and Hospitality Management (BHM), College of Professional and Continuing Education (CPCE), The Hong Kong Polytechnic University
E-mail: aris.lam@cpce-polyu.edu.hk
Dr. Macy Mei Chi Wong
Head of Employability Services, Senior Lecturer, Division of Business and Hospitality Management (BHM), College of Professional and Continuing Education (CPCE), The Hong Kong Polytechnic University
E-mail: macy.wong@cpce-polyu.edu.hk
Dr. June Ching Yan Fung
Lecturer, Division of Business and Hospitality Management (BHM), College of Professional and Continuing Education (CPCE), The Hong Kong Polytechnic University
E-mail: june.fung@cpce-polyu.edu.hk

Abstract of the track (100-200 words)

Background: The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has transformed teaching, learning, and assessment practices. While AI tools offer opportunities for efficiency, personalization, and innovation, they also raise pressing ethical, cognitive, and pedagogical challenges, including academic integrity or ethical concerns, over‑reliance, and diminished critical thinking abilities.

Subject: This track focuses on understanding ethical AI use in education through psychological, behavioral, and instructional design perspectives. Drawing on theories such as dual‑process models and the Theory of Planned Behavior, the track examines how students perceive, adopt, misuse, and rationalize AI use in academic contexts, as well as how educators can design assessment and evaluation frameworks that responsibly integrate AI technologies.

Research hotpot: Key research hotspots include AI‑induced academic misconduct (e.g., AI‑generated plagiarism), cognitive shortcuts and procrastination in AI use, ethical decision‑making in technology adoption, and the use of generative AI agents for assessment and feedback. The track aims to extend theory development, provide empirical evidence, and practical frameworks to support responsible and meaningful AI integration in higher education.

Topics

Submissions to this track may include, but not limited to, the following topics:

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