1d • General discussion
Weekend Thoughts and Resources
The strongest pattern this week is clear: the serious work is moving past “Should students use AI?” and toward “What kind of thinking must be protected, scaffolded, and assessed when AI is already in the room?” That is exactly the right battlefield for articles, workshops, and course design.
1. Cognitive offloading and critical thinking in higher education
Roy, B. K., & Jony, M. S. (2026). Generative AI and critical thinking in higher education: Students’ narratives of cognitive offloading. Journal of Interdisciplinary Studies in Education, 15(3), 263–282. doi:10.32674/jey59q25. Published May 27, 2026.
Takeaway: Students are not merely “cheating” with AI; many are outsourcing parts of cognition itself. The study found themes of superficial engagement, weakened self-regulation of cognitive effort, increasing reliance on AI-supported thinking, and student awareness that this reliance creates internal intellectual risk.
2. Character, virtue, and what universities should still do
Grove, M., & Harrison, T. (2026). Educating what AI cannot: Character and the future of higher education. British Journal of Educational Studies. Advance online publication. doi:10.1080/00071005.2026.2678972. Published June 2, 2026.
Takeaway: This is the most philosophically useful piece in the set. Grove and Harrison argue that AI exposes the poverty of efficiency-driven higher education. Their neo-Aristotelian frame places moral, civic, intellectual, and performative virtues at the center of the university’s purpose.
3. Generative AI support in open and distance learning
Öncü, S. E., Gevher, M., Erdoğdu, E., & Koçdar, S. (2026). Exploring the potential of generative AI for academic support in open and distance learning: A case study of learner experiences. The International Review of Research in Open and Distributed Learning, 27(2), 67–82. doi:10.19173/irrodl.v27i2.9289. Published May 6, 2026.
Takeaway: In an online course, students valued GenAI for time-saving and course-aligned responses, but structured use mattered. When AI use was guided by tasks, students engaged more purposefully through self-assessment and verification rather than casual answer-seeking.
4. AI as a course-design partner, not an instructional authority
Suresh, D. D., Dilek, M., Karabulut-Ilgu, A., Baran, E., & Kimber, M. (2025). Generative AI in learning design practice: Building an online course for biomedical sciences research programs. International Journal of Designs for Learning, 16(2), 244–265. doi:10.14434/ijdl.v16i2.41995. Published December 17, 2025.
Takeaway: This design case shows GenAI being used across instructional design tasks—brainstorming, scripting, narration, multimedia, and assessment—while keeping human judgment in control. The authors explicitly treated AI as a “co-thinker,” not as the designer of record.
5. Human-centered AI course design in higher education
Adair, D., & Kilgore, W. (2026, February 12). AI and course design: Machines can help, but only humans can teach. EDUCAUSE Review.
Takeaway: The practical argument is sharp: students may use AI, but they still want human support when they are struggling. The piece cites a 2025 student survey finding that 42% of students used generative AI at least weekly, while 84% still primarily sought help from a person when struggling with a course concept.
The most useful organizing claim is this: AI should be treated as a pressure test of educational purpose. If a course only asks students to produce outputs, AI will expose the course’s weakness. If a course requires inquiry, evidence evaluation, reflection, judgment, and intellectual character, AI can be integrated without surrendering the core of education.
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Weekend Thoughts and Resources
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