Theory Framework

Flexible Thinking (FT)

Definition and operational criteria

Flexible Thinking (often described as cognitive flexibility) is the capacity to adjust one’s thoughts, strategies, and behaviour in response to new information or changing conditions. In this demo it is operationalized through four measurable criteria used across all modules.

FT1 — Reframing

Shift perspectives and test alternative frames (technical / learner / policy / risk).

FT2 — Alternative generation

Generate options and switch strategy when evidence changes.

FT3 — Evidence‑based justification

Use logs/sources to justify actions; state what would falsify your hypothesis.

FT4 — Reflection & transfer

Monitor reasoning, document Plan‑B heuristics, transfer to new incidents.

Flexible Instructional Design (FID)

The three pillars of flexibility

FID is used as the learning design lens: keep learning outcomes constant, but vary routes and scaffolds. The prototype implements FID via multiple pathways, adjustable scaffolding (hint ladder / templates), and multiple evidence formats (paths, prompts, inquiry artifacts).

Pillar 1

Flexible delivery (the “How”)

Multiple representations: logs + diagrams + prompts + worked examples.

Pillar 2

Flexible pacing (the “When”)

Self‑paced branching + progressive hints; optional embeds for depth.

Pillar 3

Flexible assessment (the “Proof”)

Multiple evidence formats aligned to the same rubric (exports + snapshots).

Research snapshot

AI perceptions and flexible thinking (predictive evidence)

A recent study reported a low but statistically significant relationship between teachers’ flexible thinking skills and perceptions related to AI use in education. In their regression model, learning perception was a significant predictor (β≈0.26) and the model explained ~7% of variance (R≈0.27, R²≈0.07).

Model fit

R ≈ 0.27

Explained variance

R² ≈ 0.07

Key predictor

β ≈ 0.26

Source: EJ1483119 (ERIC PDF) and IJOPR PDF listed in the references column.

Key references

Practice guidance used in this demo

external

Edutopia — AI in education (policy & implementation)

Highlights flexible guidelines over rigid policies, iterative improvement, and focusing on fewer vetted tools paired with training.

Faculty Focus — prompting skills for critical thinking

Emphasizes clarity/specificity, iterative refinement, fact-checking, bias analysis, and avoiding over-reliance on AI.

THE Campus — interpretive flexibility & boundary objects

Frames GenAI outputs as shared “boundary objects” enabling cross‑team sensemaking; different perspectives can be productive rather than conflicting.

Predictive evidence — AI use perceptions & FT

Two PDFs used to justify the evidence-first design choices and measurement framing.

ERIC PDF IJOPR PDF

FT rubric (zoomable)