Making thinking visible: Metacognition in legal education

Dr Kryss Macleod is Head of Legal Pedagogy and Innovation at Manchester Law School, where she leads the strategic development of curriculum, assessment, and digital futures across undergraduate, postgraduate, and professional legal education. Her work sits at the intersection of legal education, professional regulation, socio-technical change, and academic leadership, with a particular focus on professional judgement, ethical reasoning, accountability, agency, and equitable outcomes.

Context

At Manchester Law School, metacognition has become a key principle in our approach to teaching design. It has underpinned progression-focused curriculum redesign, incremental assessment, digital skills development, staff development, and more recent work on teaching in AI-mediated environments.

For us, metacognition means helping students understand how learning works: how to plan a task, monitor understanding, recognise confusion, evaluate strategies, respond to feedback, and adapt their approach (Flavell, 1979; Pintrich, 2002). In legal education, students are not only learning legal knowledge but developing judgement under conditions of difficulty, uncertainty and consequence (Schön, 1983; Eraut, 1994).

This became particularly important during programme redesign following the 2018 review of the undergraduate Law programme. The review identified concerns around continuation, differential outcomes, gatekeeper units, over-assessment of some learning outcomes, and reliance on end-point assessment. In response, the School adopted design principles centred on validation, scaffolding, self-efficacy, mastery learning, incremental assessment and metacognition (Bandura, 1989, 2001; Rendon, 1994; Pintrich, 2002). The aim was to create conditions in which students could better understand and improve their own progress.

What we did

And so, we embedded metacognition as a programme design principle. Module teams were encouraged to build developmental opportunities into teaching and assessment so students could reflect on progress, use feedback effectively, and engage early with their studies.

This linked closely to the School’s ongoing work on self-efficacy. Students are more likely to persist when progress is visible, and self-efficacy is associated with motivation, persistence and achievement (Linnenbrink and Pintrich, 2000; Schunk and Pajares, 2009). Scaffolded tasks, formative checkpoints, reflection prompts and incremental assessments help students recognise how they approached a task, where they struggled, and what they need to change next time.

Ongoing staff development has translated these ideas into practical teaching design. In sessions on scaffolding and self-efficacy, colleagues identified “missing rungs” in the learning ladder: points where tasks assumed confidence, preparation or strategies students had not yet developed. This drew on the idea that scaffolding can bridge the gap between students’ current abilities and intended learning goals (Rosenshine and Meister, 1992). Colleagues reviewed assessment support, identified gaps, and designed targeted scaffolds.

Later sessions have focused more directly on metacognition through a simple model: before learning, during learning and after learning. Before a task, students may need planning prompts: What is this task asking me to do? What do I already understand? During a task, they may need monitoring prompts: What is confusing me? What evidence am I relying on? After a task, they may need evaluation prompts: What worked? What did I misunderstand? What will I change next time? This reflects evidence linking metacognitive self-regulation with critical thinking and deeper learning strategies (Garcia and Pintrich, 1992; Ku and Ho, 2010).

Digital Skills for Lawyers provides one example. The module develops digital competence alongside reflection on confidence, learning habits, ethical awareness and professional identity. The module supports metacognitive development by helping students build work gradually, record understanding, identify uncertainty and build evidence for deeper and supported reflection.

Metacognition and AI

Generative AI has increased the importance of metacognitive design. AI can provide structure and plausible answers before students have developed understanding, worked through uncertainty, or exercised judgement, and so, we need to understand how and where students are using it, even if we don’t endorse it within our classrooms. Discussing, instead of disciplining, possible AI use with students can also help us gain greater understanding of our teaching design: where do students feel supported in tasks, where they understand the value of the activities, or other areas of uncertainties in the learning process.

To support staff beyond staff development sessions, we also developed an interactive workbook, on Designing Metacognition in Education. It guides academics through three areas: academic task design, competence-focused course design, and AI-aware metacognitive design. Colleagues work directly on their own teaching, designing planning, monitoring and evaluation prompts, considering metacompetence in professional courses, and exploring how AI affects planning, verification and calibration. The workbook concludes with an action plan for a module, activity or assessment. These interventions shift attention from outcomes alone to the processes that produce them, helping both staff and students to think about how tasks and tools both shape thinking and confidence.

What we learned

Metacognition is easy to support in principle but harder to sustain in practice. It can disappear when modules become crowded, module leaders change, deadlines dominate, or staff assume students already know how to plan, monitor and evaluate their learning. It also loses value when reflection is added without changing task design.

The most effective work has been practical and situated. Rather than teaching metacognition in the abstract, staff development has focused on specific points of difficulty: workshops students arrive unprepared for, assessments they misread, legal problems they rush to solve, AI outputs they accept too readily, or feedback they struggle to use.

The workbook now supports this by making design decisions explicit. Where will students plan, monitor and evaluate? Which parts of a task require judgement rather than performance? What must students learn to check for themselves?

The key lesson is that metacognition works best when embedded throughout the learning cycle. Students need opportunities to plan before acting, monitor while acting, and regulate afterwards.

Transferable insights

Metacognition is not an add-on study skill. It is part of equitable learning design. It reduces reliance on tacit academic knowledge, makes uncertainty discussable, and helps students take a more active role in their development.

Three questions may be useful for educators in any discipline:

1. Where in this task do students need to understand their own thinking, rather than simply produce an answer?
2. What confusion, uncertainty or weak strategy needs to become visible before the final assessment point?
3. How can students be supported to change their approach, rather than simply receive feedback on an outcome? 

In an AI-mediated environment, these questions become more urgent. Students will increasingly work with systems that generate plausible outputs, but verification is not a procedural afterthought. It rests on prior cognitive and metacognitive capacities: the ability to recognise uncertainty, judge relevance, test evidence, identify absence, and know when confidence has been earned rather than supplied.

The educator’s task, then, is not to require students to check AI-generated material, but to understand and cultivate the conditions that make checking possible. That means attending closely to students’ learning processes: what they notice, what they overlook, how they form judgement, where their reasoning becomes fragile, and which tasks might help those capacities develop. Metacognition creates space for this work. It allows students to examine not only the output before them, but the assumptions, habits, and partial understandings through which they come to read it.

References

Bandura, A. (1989) ‘Human agency in social cognitive theory.’, American psychologist, 44(9), p. 1175.

Bandura, A. (2001) ‘Social Cognitive Theory: An Agentic Perspective’, Annual Review of Psychology, 52(Volume 52, 2001), pp. 1–26. Available at: https://doi.org/10.1146/annurev.psych.52.1.1.

Eraut, M. (1994) Developing Professional Knowledge and Competence. London: Routledge Falmer.

Flavell, J.H. (1979) ‘Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry.’, American psychologist, 34(10), p. 906.

Garcia, T. and Pintrich, P.R. (1992) ‘Critical Thinking and Its Relationship to Motivation, Learning Strategies, and Classroom Experience.’

Ku, K.Y. and Ho, I.T. (2010) ‘Metacognitive strategies that enhance critical thinking’, Metacognition and learning, 5(3), pp. 251–267.

Linnenbrink, E.A. and Pintrich, P.R. (2000) ‘Multiple pathways to learning and achievement: The role of goal orientation in fostering adaptive motivation, affect, and cognition’, Intrinsic and extrinsic motivation. Elsevier, pp. 195–227.

Pintrich, P.R. (2002) ‘The role of metacognitive knowledge in learning, teaching, and assessing’, Theory into practice, 41(4), pp. 219–225.

Rendon, L.I. (1994) ‘Validating culturally diverse students: Toward a new model of learning and student development’, Innovative higher education, 19(1), pp. 33–51.

Rosenshine, B. and Meister, C. (1992) ‘The use of scaffolds for teaching higher-level cognitive strategies’, Educational leadership, 49(7), pp. 26–33.

Schön, D.A. (1983) The reflective practitioner: how professionals think in action. New York: Basic Books.

Schunk, D.H. and Pajares, F. (2009) ‘Self-efficacy theory’, Handbook of motivation at school. Routledge, pp. 49–68.