Designing Learning That Adapts: A practical framework for adaptive, scenario-based learning across classrooms, e-learning platforms and AI-supported education

Designing Learning That Adapts presents a practical framework for adaptive, scenario-based learning that can be applied across classrooms, e-learning platforms and AI-supported education. Drawing on established learning science, instructional design practice and the questions raised by more than a decade of e-learning product work, the paper sets out principles for shaping learning around the learner's goal, starting point, prior knowledge and preferred ways of learning, and for choosing the right mode (text, voice, video, interaction and dialogue) for each task. It frames adaptive and scenario-based approaches as design practices to be observed, tested and revised rather than as proven efficacy claims, and offers a structured way for educators, platform teams and workplace learning designers to apply these ideas in their own contexts. This is a practitioner framework and does not report an empirical efficacy study.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23669203
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Designing Learning That Adapts: A practical framework for adaptive, scenario-based learning across classrooms, e-learning platforms and AI-supported education

Vincent Chidozie Okeke, Chidi Jeremiah Okeke, Josephine Uchechukwu Okeke
Zenodo (CERN European Organization for Nuclear Research)
Intelligent Tutoring Systems and Adaptive Learning
article

Designing Learning That Adapts: A practical framework for adaptive, scenario-based learning across classrooms, e-learning platforms and AI-supported education

Vincent Chidozie Okeke, Chidi Jeremiah Okeke, Josephine Uchechukwu Okeke
article en

Abstract

Designing Learning That Adapts presents a practical framework for adaptive, scenario-based learning that can be applied across classrooms, e-learning platforms and AI-supported education. Drawing on established learning science, instructional design practice and the questions raised by more than a decade of e-learning product work, the paper sets out principles for shaping learning around the learner's goal, starting point, prior knowledge and preferred ways of learning, and for choosing the right mode (text, voice, video, interaction and dialogue) for each task. It frames adaptive and scenario-based approaches as design practices to be observed, tested and revised rather than as proven efficacy claims, and offers a structured way for educators, platform teams and workplace learning designers to apply these ideas in their own contexts. This is a practitioner framework and does not report an empirical efficacy study.

Zenodo (CERN European Organization for Nuclear Research)
Cochlear (Australia) (AU)
Openalex Percentile: Top 13%
Intelligent Tutoring Systems and Adaptive Learning
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