Deep dives
The flagship topic treats digital learning as a public argument, not a product category. Kim Flintoff’s selections move between institutional continuity, belonging, platform power, AI-assisted design, screen fatigue and the quiet question underneath them all: what kind of learning is technology actually making possible?
How Big Tech Captured American Schools
The human layer matters. Posts on belonging and connection sit beside policy pieces about continuity planning and disruption, which gives the topic a practical, institutional feel. Digital learning appears here as a matter of resilience and care as much as delivery.
Belonging first: creating space for connection in learning and teaching – Teaching@Sydney
AI enters the topic with caution. Frameworks and course-design posts are present, but the strongest through-line is judgment: machines may help, yet teaching remains a human and ethical act.
A Practical SAMR + AI Framework for Instructional Design
In the assessment topic, AI is not a side issue; it is the stress test. The feed follows how exams, detection software, oral assessment, misconduct processes and authentic tasks are being forced into revision by tools that can now perform in ways institutions did not design for.
Our research shows how AI is getting better at exams. Here’s how unis can respond
Kim’s selections are skeptical of simple enforcement. Items questioning AI-detection software and its harms sit beside posts on cheating, anxiety and policy reform, creating a sharper picture of integrity as a design problem rather than a surveillance problem.
Unis and schools are moving away from AI-detection software. They should stop using it altogether
The topic keeps returning to reform. It asks whether assessment should move toward projects, oral defense, in-person exams, or more authentic evidence of learning. The answer is not presented as settled, but the old exam culture is clearly under pressure.
Generative AI use and misuse call for assessment reform in higher education
The AI in Education topic reads like a governance map. It tracks faculty fluency, student writing, academic libraries, AI marking, misconduct claims and institutional anxiety, with a steady preference for practical consequences over spectacle.
From Fear to Fluency: Helping Faculty Navigate AI
Labor and legitimacy are recurring concerns. Posts about AI marking, automated teaching substitutes and professors examined for AI use show that the disruption is not only about students cheating; it is also about professional trust, academic work and what counts as teaching.
Academics and AI marking: a welcome aid for overworked staff or a sloppy shortcut?
The selections also widen the lens beyond classrooms. Libraries, search practices and student strategy appear as sites of change, suggesting that AI is reshaping the whole learning infrastructure, not just assignments.
From Search to Strategy: What Student AI Use Means for the Future of Academic Libraries