Diversify AIED: use diverse technologies, address big problems, and reuse existing resources rather than reinventing content¶

Strategy · All strategies
Evidence · no claims cited

Description¶

As concrete revolutionary moves, the article recommends broadening beyond desktop computers to handhelds, wearables, robotics, and mobile sensors for new interaction styles; applying educational data mining and learner modelling to open activities needing just-in-time support and personalization; and building ILEs as shells over existing resources instead of developing decontextualized content, which is labour intensive.

Design Implications¶

Context¶

Requirements¶

• Building on existing resources requires bridges to sibling communities such as the Learning Sciences.

Constraints¶

• All but one of the reviewed papers used a desktop or laptop computer and targeted formal school settings, indicating how far current practice is from this recommendation.

Target Learners¶

• classroom learners
• informal learners
• workplace learners

Target Learning Goals¶

• collaboration
• personalized learning
• learning in authentic contexts

Related Strategies¶

• Advance AI-motivation research through theory-driven, pedagogically explicit, longitudinal designs covering broader motivational constructs
• Adopt unified KPIs and rigorously assess learner diversity, recommender fairness, and long-term engagement when evaluating ITS
• Future AI-SRL research should broaden search strategies, include educators and younger learners, address motivation, and apply longitudinal designs
• Use data mining in education research in a principled, goal-driven way informed by domain knowledge

Examples¶

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Key Sources¶

• Ido Roll & Ruth Wylie. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26, 582-599. https://doi.org/10.1007/s40593-016-0110-3