Author Information
Analisa Hamdan, Asia Pacific University of Technology & Innovation, MalaysiaAbtar Darshan Singh, Asia Pacific University of Technology & Innovation, Malaysia
Aisyamariam Abdul Uzza, University of St Andrews, United Kingdom
Fumiko Konno, Asia Pacific University of Technology & Innovation, Malaysia
Fahd Ali Raza, Asia Pacific University of Technology & Innovation, Malaysia
Abstract
Across the world, higher education lecturers face growing pressure to redesign assessments due to students' widespread use of Generative AI (Gen AI) tools like ChatGPT, Copilot, and Gemini. Despite institutional restrictions, students still use Gen AI tools, raising concerns about the authenticity, fairness, and relevance of traditional assessments. Conventional models often fail to foster the higher-order thinking, creativity, and authentic skills needed in AI-integrated learning. This qualitative, exploratory study investigates how lecturers embed Gen AI in assessment redesign. Using Technological Pedagogical Content Knowledge (TPACK) framework, the analysis considers how lecturers balance pedagogy, technology and content when configuring or discouraging AI support. Biggs' Constructive Alignment model assesses task coherence with learning outcomes, while Furze's AI Assessment Scale classifies AI transparency, from no AI to full AI, defined as explicit disclosure and embedding of AI assistance. Semi structured interviews with 20 STEAM lecturers at Malaysian private universities were purposively sampled and thematically analysed in NVivo, identifying four key themes. Findings show that Gen AI scaffolds learner agency, personalises feedback and stimulates higher order cognition, yet lecturers wrestle with tensions between empowerment and academic integrity. Adoption is hindered by inconsistent policies, limited professional development and digital equity challenges. Discipline specific variations reveal emerging best practices: for example, re authoring a reflective essay prompt into an AI assisted critique task demonstrates how theory guided redesign, AI classification and outcome alignment. The study recommends revising curricula, training faculty, clarifying AI guidelines, and supporting students to ensure pedagogically sound and ethical assessment in the AI era.
Paper Information
Conference: ACE2025Stream: Assessment Theories & Methodologies
This paper is part of the ACE2025 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window
To cite this article:
Hamdan A., Singh A., Uzza A., Konno F., & Raza F. (2026) Investigating Gen-AI Integration in Assessment Redesign Among Higher Education Lecturers ISSN: 2186-5892 – The Asian Conference on Education 2025: Official Conference Proceedings (pp. 1499-1513) https://doi.org/10.22492/issn.2186-5892.2026.115
To link to this article: https://doi.org/10.22492/issn.2186-5892.2026.115
Comments
Powered by WP LinkPress