How Should AI Be Designed Into the Learning Experience?

Start with the learning goal and the learning process — not the AI feature. The knowledge base's Pedagogies and Teaching Strategies concept emphasizes that the same AI can function as a scaffold, Socratic interlocutor, feedback partner, Simulation, or answer generator depending on the instructional design. What matters is whether the configuration preserves the activity that produces the intended learning.

A strong default pattern

A strong default pattern is: learner attempts → AI supports → learner evaluates or revises → learner demonstrates understanding. More concretely:

• Preserve productive failure where it serves learning.
• Ask for an initial prediction or solution before displaying AI assistance.
• Favor questions, hints, examples, counterarguments, and feedback over immediate completion.
• Require verification of consequential claims.
• Incorporate opportunities for explanation and teach-back.
• Gradually fade support as competence grows.
• Retain some AI-free opportunities for learners to calibrate what they can do independently.

The Reducing AI Misuse synthesis specifically recommends think-first/AI-second/reflect sequences and deliberate evaluation checkpoints.

Match the AI role to the level of cognitive engagement

Rummel, Nachtigall and Panadero's kitchen-machine analogy reframes the design question from whether learners use generative AI to how that use shapes what they become. Mapping four uses of a smart kitchen appliance onto learning cases via the ICAP and SAMR frameworks gives a design ladder: fully outsourcing an assignment, without revision or critical engagement, is Passive substitution and risks skill loss and over-reliance; refining prompts and cross-verifying outputs requires prior knowledge and self-regulation (Active/Augmentation); using AI to brainstorm, outline and evaluate original work is Constructive/Modification; and AI as a genuine dialogue partner for co-construction and adaptive Feedback is Interactive/Redefinition. The design implication is direct: the same tool is a bypass at one rung and a scaffold at the next, so specify the intended mode rather than granting blanket access.

Sequence the design, don't just permit the tool

Divjak, Svetec and Horvat analyzed the planned sequence of 29,064 teaching and learning activities across 554 courses and found a visible design grammar: Acquisition-type activities are the most common entry point and the largest single type (above 20%), learning type tracks the intended Bloom level (Acquisition falling from ~50% at level 1 to ~20% at level 6, Production rising above 20% at levels 5–6), and the strongest transition is Assessment → Discussion (0.332). Two lessons for AI design: AI belongs where the sequence intends a specific activity type rather than bolted on at the end, and because feedback clustered with collaboration, group work and teacher presence, peer and synchronous structures create the Feedback moments AI support should plug into rather than replace.

Torres-Sahli and colleagues' "refrain, then amplify" framework pushes this to program level: withhold a generative tool while a capacity is forming, then restore it once the student can direct it, judge what it returns, and answer for it, with a hard-to-fake checkpoint at each hinge. Devices are governed by a forming-versus-offloading criterion — allowed where they support engaged work, excluded where they drain attention. This turns offloading decisions into a curriculum and AI Governance question that precedes, rather than follows, course-level design.

Constructive alignment comes first

McInnes and colleagues' discourse analysis of 14 pieces of higher-education guidance warns that efficiency-framed advice — using generative AI to draft outcomes, rubrics and course outlines — produces alignment that looks aligned while neglecting the "constructive" half: outcomes, activities and Assessment generated as discrete items rather than interdependent ones. Their remedy is re-sequencing, not prohibition: educators should understand constructive alignment well enough to direct, interrogate and reject AI output before delegating any part of it, because a surface-acceptance habit based on plausibility is the same evaluative failure instructors warn students against. Where AI is used, they argue for institutionally bounded, retrieval-augmented systems configured around local policy and quality standards rather than generic internet-trained defaults.

The broader principle

The broader principle in the Principled AI Education Framework is that technology should augment rather than displace human capabilities that education intends to develop. See also Instructional Design, Active Learning and Scaffolding.

For the pedagogical defaults that determine whether a designed interaction preserves learning, see How Can I Reduce AI Cheating in My Course? and How Do I Redesign Assessment So That a Grade Still Tells Me Something Defensible About What the Student Knows or Can Do?; for how the same principles constrain the software itself, see What Are Best Practices and Tips for Designing Effective Educational AI Software?, and for their translation into a tutor's architecture, see What Are Best Practices for Developing an Effective AI Tutor?.