Below are some suggested exercises for students when learning WITH AI.

The 10 Principles of Substantive Learning

The trap of the illusion of learning is real, but there is a the way out. Below, I’ve put together 10 evidence-based principles that convert “feels like learning” into actual gains in memory, understanding and transfer.

Think of them as a design spec for every AI-assisted study session—and a product checklist for anyone building learning tools. Each principle is grounded in decades of research (I’ll note the effect size so you can see the practical impact: roughly 0.2 = small, 0.5 = medium, 0.8 = large) and paired with a simple “use ChatGPT like this” prompt.

Taken together, they add the right kind of friction—retrieval, self-explanation, interleaving, feedback, spacing—so your time with AI stops polishing answers and starts building durable skills.

Here’s what this might look like in practice:

1) Embrace the struggle: the “desirable difficulty” zone

What: Learning sticks when it’s effortful but doable; too easy breeds a “familiarity illusion,” too hard leads to disengagement.

Why: Guided discovery with scaffolding shows medium effects (d ≈ 0.40–0.50) because it keeps learners in that productive challenge zone (Alfieri et al., 2011).

Try this when Learning with AI:

Difficulty ladder: “Here’s a solved example [paste]. Make a new one with the same concept but different numbers → add one twist → turn it into a word problem.”

2) Do before you know: productive failure / problem-based learning

What: Attempting a problem before instruction “primes” your brain to value the explanation.

Why: Problem-based learning improves transfer (d ≈ 0.30–0.50) (García et al., 2021), and productive failure yields d ≈ 0.36 (Loibl et al., 2017).

Try this when Learning with AI:

Safe simulation: “Pose a novel problem. Don’t teach me yet. Let me try. Then offer a minimal hint → deeper hint → full solution only after I explain my approach.”

3) Treat content as a resource, not the destination

What: Reading ≠ learning. Use content to solve an active problem you’ve already tried.
Why: Prior struggle creates a “need to know,” deepening processing (Loibl et al., 2017).

Try this when Learning with AI:

Pre-reading primes: “Before I read this French Revolution chapter, generate three analytical questions I probably can’t answer yet to focus my reading.”

4) Practise how you’ll perform: authentic assessment

What: You learn what you practise. Make practice mirror the final performance.

Why: Performance-based assessment predicts future performance with large effects (d ≈ 0.80–1.00) (Gulikers et al., 2004).

Try this when Learning with AI:

Case → memo: “From this article [paste], create a 1-page CEO case with a decision point. I’ll write a memo. Grade me against this rubric [paste].”

5) Close the loop: feedback as a superpower

What: Targeted, actionable feedback is the single biggest lever.

Why: Meta-analyses show medium average effects (d ≈ 0.40) when feedback answers: Where am I going? How am I going? Where to next? (Hattie & Timperley, 2007).

Try this when Learning with AI:

Three-question frame: “Act as a writing tutor. Assess only my thesis and hook using: goal → current performance → concrete next steps.”

6) Make memory do the work: retrieval practice

What: Pulling information from memory beats re-reading for long-term retention.

Why: Practice testing delivers medium-to-large effects (d ≈ 0.46–0.65) (Adesope et al., 2017).

Try this when Learning with AI:

Varied retrieval: “Using the doc attached [paste] quiz me on the Krebs cycle with (1) a “near miss” MCQ, (2) a fill-in-the-blank, (3) an explain-it-to-a-teen in my own words.”

7) Defeat the cram: spacing

What: Space study sessions so you revisit topics just before you forget them.

Why: Spacing yields medium effects (d ≈ 0.42) on long-term retention (Cepeda et al., 2006).

Try this when Learning with AI:

Plan and design for spacing: “I have a mater’s level exam in 6 weeks on derivatives, integrals, series [curriculum attached]. Build a weekly plan that uses the spacing method to resurface older topics at optimal intervals. For each weekly plan, include suggested content and activities to help me to learn via the spacing method [attach doc on the spacing method].”

8) Build flexible knowledge: interleaving

What: Mix problem types so you must select the right strategy each time.

Why: Interleaving outperforms blocked practice with d ≈ 0.45–0.50 (Brunmair & Richter, 2019).

Try this when Learning with AI:

Plan and design for interleaving: “I have a mater’s level exam in 6 weeks on derivatives, integrals, series [curriculum attached]. Build a weekly plan that uses the interleaving method to test my ability to apply key concepts in a variety of contexts. For each weekly plan, include suggested content and activities to help me to learn via the interleaving [attach doc on interleaving].”

9) Learn together: social cognition & the protégé effect

What: Explaining to others surfaces gaps and consolidates understanding.

Why: Cooperative learning shows strong effects when designed with positive interdependence and individual accountability (d ≈ 0.64) (Johnson et al., 2000).

Try this when Learning with AI:

Debate prompts: “Moderate a CRISPR ethics debate. Provide 3 propositions, each with a brief ‘for’ and ‘against’ to kick us off, then facilitate.”

10) Respect bandwidth: manage cognitive load

What: Working memory is limited; reduce extraneous load so you can invest in germane load (schema building).

Why: Worked examples and thoughtful sequencing show medium-to-large effects (d ≈ 0.50–0.60) (Sweller & Chandler, 1994).

Try this when Learning with AI:

Progressive disclosure: “Teach me the Krebs cycle [doc attached] in layers: one-sentence purpose → high-level analogy → main stages → detailed steps.”