Critical thinking - some models to inform teaching and learning

 The application of critical thinking towards evaluating information has been and is now an even more crucial skill. AI generates plausible responses but these must always be checked or triangulated. Ensuring everyone understands the underlying principles of critical thinking, and how the process to apply it towards becoming better equipped to deal with the avalanche of material many people wade through daily, is now more important than ever.

Three recent articles provide guidance. Each offers a model/framework to teach and preserve critical thinking skills.

1) 

Vendrell, M. & Johnston, S-K. (2026). Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education, Computers and Education: Artificial Intelligence, Volume 10, https://doi.org/10.1016/j.caeai.2026.100572

Firstly, an update on critical thinking from Vendrell and Johnston (2026). The paper presents an analysis of research in cognitive psychology, educational theory and AI ethics. These are then synthesised to develop a pedagogical framework for integrating LLMS into teaching and learning. The principles of critical engagement are defined as: conceptual interpretation, intellectual curiosity, inferential reasoning, evaluative judgement, metacognitive regulation, and epistemic integrity. The paper recommends design principles that preserve cognitive friction, scaffold LLMs as partners, embed evaluation as standard practice, activate metacognitive regulation, encourage humility and curiosity, foster epistemic integrity, align assessment with thinking and balance AI mediated and AI free phases. 

2) 

Brcic. M. & Frljic, S. (2026). The effortless trap: Productive struggle, AI, and the illusion of learning. https://arxiv.org/pdf/2606.26181

A preprint which extends on the article in 1). it advocates for ways to avoid cognitve debt but ensuring that there are elements of 'productive struggle' in learning. The framework involves working through the following when working through new concept:

- Prime - help learners find a reason/motivation 

- Probe - provide a 'hard' problem so that learners have to tryout new ideas, learn from making mistakes and problem solve their way through.

- Point - using Socratic guides or cognitive apprenticeship to help make the problem solving strategies visible

Attach - connect the new to already learnt or new concept

Strengthen - reinforce through deliberate practice so that the concept/process is anchored.

3) 

Liubertaitė AD (2026) EFFORT-AI: an effort-preserving module for cognitively aligned human-AI learning. Frontier of Education. 11:1849821. doi: 10.3389/feduc.2026.1849821

This article also advocates for the need to ensure cognitive effort is undertaken to assure learning takes place. Introduces the EFFORT-AI model as a way to iintegrate AI into learning without allowing AI to do all the work.

- Elicit - Help learners make known to themselves their prior knowledge on the topic and their motivations to learn.

- Formulate - undertake a first attempt at a problem, either through retrieval or explanation

- Feedback - use feedback cycle to find out progress (or not)

Organise - make sense of the concept/process

- Reflect - review, revise and work out how learning undertaken , gaps in understanding and where to move to next

- Transfer - apply and review (spaced) to keep the concept/process active.

Articles 2) and 3) provide examples, mostly in STEM topics but frameworks cane be adapted to many other disciplines. The frameworks in themselves are therefore useful as starting points for adapting signature disciplines and pedagogy towards pedagogical integration of AI.