Vision, challenges, roles and research issues of Artificial Intelligence in Education

Hwang, Xie, Wah, and Gasevic's paper is a concise agenda-setting article for AIED as a field. It begins from the rapid advancement of computing technologies and defines AIED as the use of AI technologies or applications in educational settings to facilitate teaching, learning, and decision making. The paper is valuable because it does not treat AIED as simply adding AI to education. It frames AIED as an interdisciplinary research area where educational needs should guide technology design.

The authors emphasize several roles for AI in education. AI can provide personalized guidance, support, and feedback to students. It can assist teachers by making learning processes more visible, supporting assessment, and reducing some routine workloads. It can also help policymakers or administrators make decisions, although those uses require careful attention to fairness, validity, and consequences. This multi-role framing is useful for AIEDHK because it fits the platform's research-to-product mission: student-facing, teacher-facing, and system-facing AI should be analyzed differently.

A major contribution of the paper is its implementation framework. Rather than presenting AI as a universal solution, the authors argue that researchers need to consider learning and teaching settings, educational needs, AI roles, and disciplinary perspectives. This matters because AIED sits at the intersection of computer science and education. A technically strong model may fail if it does not address a real learning need; an educationally desirable idea may fail if the AI method is not valid, scalable, or interpretable.

The paper is useful for editorial work because it gives AIEDHK a set of classification questions. What setting is being addressed: formal classroom, online learning, assessment, tutoring, or institutional management? Whose role is being supported: student, teacher, researcher, administrator, or policymaker? What kind of AI function is being proposed: prediction, recommendation, diagnosis, feedback, content generation, or decision support? What evidence would make that function trustworthy? These questions make the article more than a field overview. They turn it into a review protocol for future Research News entries, especially when comparing student-facing AI assistants with teacher-facing analytics or policy-facing decision tools.

The paper also outlines research issues for the field. These include personalization, feedback, adaptive learning, teacher support, learning analytics, assessment, ethics, data use, and interdisciplinary collaboration. For a Research News page, it can serve as a framing piece that tells readers what kinds of questions the field should ask. For Hong Kong, the paper supports a practical agenda: does a paper support student learning directly, support teacher decision-making, improve assessment, help administrators, or clarify governance? The article's central value is that it keeps education in front of AI and argues for systems designed around learning goals, teaching contexts, and decision needs. It is therefore a useful closing paper in this collection because it turns individual studies into a field-level agenda. It also gives AIEDHK language for judging whether a new paper contributes a tool, an evidence claim, a design principle, or a governance warning.