Plenary: AI Technology and Higher Education

Tuesday 21 October 2025
14:00 – 15:15
Thaksin University

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The ‘Glocalisation’: Functional Role of Higher Education from Local to Global

Sub-themes in focus: AI Technology and Higher Education: “Shaping Higher Education with AI: Opportunities, Challenges, and Next Steps for LLMs”

What: Roundtable Discussion: Plenary Session

Overview: In these sessions we will explore the sub-themes for the Congress through a range of short presentations leading into round table discussions.

Each parallel session will have a Chair to facilitate the discussion and we hope the conversations might spark areas for future WTUN collaboration and workstream developments.

Chair: TBC

Speakers:

• Professor Mansoor Alaali, President, Ahlia University
• Assistant Professor Dr Pantip Piyatadsananon, Director of Technopolis, Suranaree University of Technology
• Professor Masaomi Kimura, College of Engineering, Computer and Communications Engineering, Shibaura Institute of Technology

Joining Details: TBC

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Professor Mansoor Alaali

‘Strategy of Future Higher Education with AI’

We stand today at a pivotal crossroads in human history, where machines are no longer just tools, but intelligent agents capable of learning, reasoning, and shaping our world in ways that carry profound implications for governments, industries, education and society at large.

Artificial intelligence is increasingly emerging as a strategic force capable of transforming how we teach, learn, govern and grow. Universities are now being called to rethink their core functions through the lens of intelligent systems, from curriculum design and student engagement to institutional management and research innovation.

As AI reshapes the landscape of global competition and collaboration, it also amplifies the tension between opportunity and risk. A visionary integration of AI in higher education is no longer optional, it is essential. In response, I am proposing a forward-looking model for the anticipated future: one that positions intelligent systems at the centre of institutional strategy while upholding the academic values that must continue to define our purpose.

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Assistant Professor Dr. Pantip Piyatadsananon 

‘Instructional Strategies and Learning Enhancement in the Era of Student Reliance on AI’

In the era where artificial intelligence (AI) plays an increasingly dominant role in students’ learning processes—particularly in completing assignments and reports—analytical subjects such as Location Analysis face a new pedagogical challenge: how to cultivate genuine critical thinking and decision-making skills among learners. This article proposes five innovative instructional approaches designed to empower students to use AI wisely while enhancing their spatial reasoning and analytical competence. These approaches include: (1) integrating real-world case studies to develop context-based spatial decision-making; (2) applying comprehensive analytical frameworks to encourage logical connections between multi-dimensional factors; (3) incorporating AI as a tool for comparative analysis, rather than as a source of definitive answers; (4) implementing simulation and Design Thinking activities that promote creative problem-solving in role-based scenarios; and (5) fostering metacognitive reflection to raise awareness of one’s own thought processes and learning strategies. The article argues that effective Location Analysis education in the age of AI requires instructional designs that prioritize thinking over answers, encouraging students to analyse, synthesize, and make data-informed decisions—while using AI as a thinking partner, not a replacement.

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Professor Masaomi Kimura

‘Deep Learning Assessment in Higher Education: Case Studies’

With the growing availability of educational data, deep learning techniques (and large language model, LLM) have emerged as a powerful approach to enhancing assessment practices in higher education. In my presentation, I introduce deep learning–based methods designed to support automated and data-driven assessment of student performance and learning outcomes. Through case studies, we demonstrate how deep learning techniques can be applied to analyze complex educational datasets, providing insights beyond traditional evaluation methods, as well as their related challenges.