Here is part 2 of a series of summaries of the provocative essays published in UNESCOs AI and the Future of Education 
Part one is here https://pontydysgu.eu/2025/10/explainable-disruptions-dilemmas-and-directions/

Page 59: Challenging hyper-personalization: Towards (re-)socializing learning (Carla Aerts)

Carla Aerts cautions against AI-driven hyper-personalization, which risks isolating learners in algorithmic echo chambers and eroding the social fabric of learning and killing the social, collaborative spirit of the classroom. Instead, the author suggests using AI to help students work together better, improving conversation and teamwork rather than replacing it.

Page 65: Infantilizing, echo chamber, or the dawn of a new enlightenment (Paul Prinsloo)

Paul Prinsloo critically examines the multifaceted nature of AI-enabled personalized learning, exploring its various architectures, levels of automation, and mixed evidence of impact. He highlights ethical risks like bias and erosion of autonomy but cautions against outright dismissal, arguing for a nuanced approach that respects student agency and is transparently designed based on clear empirical evidence and ethical guidelines. The key is to be thoughtful, transparent, and always put the student's well-being first.

Page 76: The end of assessment as we know it: GenAI, inequality and the future of knowing (Mike Perkins and Jasper Roe)

Perkins and Roe argue that generative AI exposes the vulnerabilities of traditional assessment, potentially leading to its collapse. They warn this will deepen global inequities, as digitally advantaged contexts  (wealthier schools) can access AI-integrated assessments while marginalized contexts may be forced to rely on outdated, proctored exams. They emphasize that the future of assessment is a question of power, and call for intentional efforts to redistribute resources and promote multilingual AI models.

Page 81: The ends of tests: Possibilities for transformative assessment and learning with generative AI (Bill Cope, Mary Kalantzis and Akash Kumar Saini)

Cope, Kalantzis, and Saini offer an optimistic vision of AI transforming assessment from a focus on superficial learning to continuous, formative feedback for deep learning. They introduce "cybersocial learning," where instead of high-pressure tests, AI can provide constant, helpful feedback to students as they work. The teacher guides the AI, using it as a tool to help every student succeed.

Page 90: Keeping the primary goals of education in the AI era: What do educators need to consider? (Ching Sing Chai, Jiun-Yu Wu and Thomas K.F. Chiu)

Chai, Wu, and Chiu analyze AI in education through five lenses: relational, teleological, epistemic, psychological, and pedagogical. The article argues that while AI can help teach facts and skills, the heart of education is the unique relationship between a teacher and a student. This human connection builds trust, respect, and independence in a way a machine cannot. AI should be used to support this relationship, not replace it.