Reusable, evidence-based instructions your AI teaching assistant can retrieve and apply in any conversation — from error analysis to retrieval practice. Expand a card to preview the full skill, or open its page for the complete guide.
Redesign teaching sequences to embed productive struggle before instruction, leveraging generation and desirable difficulties to deepen conceptual understanding and transfer.
By Gareth Manning
This skill transforms traditional "teach-then-practice" sequences into "struggle-then-consolidate" models that produce stronger, more durable learning. Rather than explaining a concept before students attempt problems, productive failure requires students to generate solution approaches before receiving instruction. Research shows this generates deeper understanding, despite producing initially lower performance.
Kapur's Productive Failure Framework (2008, 2016): Students attempt novel, challenging problems before instruction, generating multiple approaches in small groups. The teacher then provides direct instruction comparing student-generated methods to the canonical solution. Kapur found students in this condition significantly outperformed direct-instruction-first groups on "conceptual understanding and transfer — even though immediate procedural tests favoured direct instruction."
Bjork's Desirable Difficulties (1994, 2011): Learning conditions that reduce immediate performance but enhance long-term retention. Four key mechanisms: spacing (distributed practice), interleaving (mixed problem types), generation (producing rather than receiving answers), and retrieval practice (testing oneself). These make learning feel harder while strengthening memory traces.
Performance vs. Learning Distinction (Soderstrom & Bjork, 2015): Performance (what students demonstrate now) differs from learning (long-term knowledge change). Desirable difficulties reduce visible performance during lessons but enhance invisible learning—measurable only later through delayed tests and transfer tasks.
Phase 1 — Generation: Students attempt tasks without prior instruction, activating prior knowledge and revealing its limits. The goal is productive struggle, not correct answers. Teachers observe and collect student approaches without intervening.
Phase 2 — Consolidation: Direct instruction explicitly compares student-generated approaches to the correct method, highlighting what worked, what failed, and why the canonical solution addresses those limitations.
Best for conceptual understanding in STEM domains (strongest evidence in mathematics). Less suitable for purely procedural skills or domains without robust productive failure research. Requires teacher expertise in the content.
Analyse student work against criteria to identify specific gaps between current performance and learning objectives, classifying each gap by type and generating targeted teaching steps.
By Gareth Manning
This skill operationalises formative assessment by systematically examining student work to identify what is missing and why. Rather than generic feedback ("needs improvement"), it classifies gaps as conceptual (misunderstanding the underlying idea), procedural (understanding the concept but erring in execution), or communication (understanding but unable to express it adequately). Each classification determines a different teaching response.
The approach is grounded in Sadler's (1989) framework requiring students and teachers to understand learning goals, assess current position relative to those goals, and take targeted action to close gaps. Hattie & Timperley (2007) emphasised that "effective feedback must address three questions: Where am I going? How am I going? Where to next?" Most teacher feedback addresses only the first two; gap analysis ensures the third—specific actionable next steps—receives equal attention.
Heritage (2010) demonstrated that "different gap types require different interventions."
Gap analysis with classified gaps, identified strengths, prioritised next teaching steps, and a feedback script communicating findings to promote improvement rather than discouragement.
Design a checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.
By Gareth Manning
This skill generates evidence-based checking-for-understanding protocols that move beyond superficial "Does everyone understand?" toward diagnostic assessment. Rather than merely confirming student attention, it produces actionable techniques paired with decision trees specifying how to respond based on results.
The tool addresses a fundamental gap in classroom practice: most CFU consists of optional hand-raising, which checks only volunteering students while leaving the majority's understanding unmeasured. This skill instead designs protocols ensuring ALL students respond, using mini-whiteboards, finger votes, cold calling with thinking time, and targeted hinge questions—each matched to specific lesson moments.
Required:
Optional: class size, known misconceptions, student profiles, available resources
Chains with: Explicit Instruction Sequence Builder, Hinge Question Designer, Formative Assessment Technique Selector, Retrieval Practice Generator
Audit a lesson plan against UDL's three principles—engagement, representation, and action/expression—identifying access barriers and suggesting concrete, prioritised modifications.
By Gareth Manning
This skill evaluates existing lesson plans through a Universal Design for Learning lens, functioning as a barrier analysis tool rather than a compliance checklist. It examines how design choices may unintentionally exclude learners based on information presentation, response formats, and motivational elements.
The UDL Lesson Auditor takes a completed lesson plan and learner context description, then produces:
The framework originates from CAST research (Rose & Meyer, 2002; CAST, 2018; Meyer, Rose & Gordon, 2014). While implementation research remains primarily quasi-experimental and case-study based, individual components—multiple representations, student choice, flexible assessment—draw support from multimedia learning theory, self-determination research, and formative assessment studies. UDL reduces barriers through intentional design; it does not guarantee access for all learners or replace specialist assessment.
The audit works from described learner variability, which is necessarily incomplete. Teacher knowledge of actual students surpasses any general assessment. For learners with identified needs requiring specialist evaluation, UDL modifications represent foundational design, not complete support.
Generate argument structure scaffolds using Toulmin, PEEL, or CER frameworks for teaching argumentative writing across subjects.
By Gareth Manning
This skill produces discipline-specific argument scaffolds tailored to student level, subject context, and requested framework. Each scaffold includes labelled structural sections with guiding prompts, rhetorical function explanations, sentence starters that model argumentative thinking, and annotated examples showing realistic student-level application.
Research establishes that argument structure requires explicit instruction:
Required:
Optional: subject area, student profiles, text type, example evidence
Chains well with: Disciplinary Writing Scaffold, Critical Thinking Task Designer, Feedback Quality Analyser, Worked Example Fading Designer
Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.
By Gareth Manning
This skill generates evidence-based retrieval practice questions designed to strengthen long-term retention through genuine knowledge reconstruction rather than recognition. It distinguishes between free recall (no cues), cued recall (partial scaffolding), and recognition formats, calibrating the mix based on student level and time elapsed since initial instruction.
The testing effect represents one of cognitive psychology's most robust findings. Research demonstrates that "retrieval practice produces substantially better long-term retention than re-studying" (Karpicke & Roediger, 2008). Rowland's meta-analysis of 159 studies found a mean effect size of 0.50 for testing versus restudy across diverse populations. Critically, classroom-based studies with middle school students confirm these laboratory effects transfer to real educational settings, and Dunlosky et al. (2013) rated practice testing as one of only two "high-utility" learning strategies.
Questions must require genuine reconstruction from memory—not surface-level pattern recognition. Teachers provide the topic, student level, and desired question count. Optional contextual inputs include student profiles, curriculum frameworks, time since learning, and documented misconceptions.
The skill cannot verify factual accuracy against specific syllabi; free recall questions may overwhelm lower-performing or EAL learners; and real-world scheduling constraints may necessitate adjusting theoretical spacing intervals. Teacher judgment remains essential.
Generate structured reflection prompts calibrated to specific teaching experiences, guiding teachers from description through analysis to critical reflection and action planning.
By Gareth Manning
This skill produces layered reflection prompts designed for a particular teaching challenge, moving practitioners systematically from surface observation ("What occurred?") through analytical reasoning ("Why did it occur?") to critical examination ("What beliefs am I holding?") and finally to concrete action planning ("What will I change?").
Research shows that professionals don't automatically learn from experience—they learn by reflecting deeply on it. As Schön emphasised, "professionals do not learn primarily from experience—they learn from REFLECTING on experience." Without structured reflection, decades of practice can amount to one year repeated many times.
Required:
Optional: teacher context, emotional response, reflection depth, time available
Generate progressive questioning sequences that develop conceptual understanding through guided inquiry rather than direct instruction, moving learners from current understanding to deeper positions through their own reasoning.
By Gareth Manning
This skill distinguishes Socratic questions (probing reasoning and assumptions) from leading questions (funneling toward predetermined answers). It creates branching dialogue trees anticipating multiple student responses with contingent follow-ups—work most educators cannot accomplish spontaneously.
Required:
Optional: target understanding, student profiles, time available, subject area
Structured sequences including:
Questions progress from concrete examples toward abstraction and principle identification. The approach maintains genuine exploratory dialogue where multiple valid answers remain possible throughout.
Design an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.
By Gareth Manning
The protocol distinguishes between three error types: procedural (incorrect method application), conceptual (fundamental misconception), and careless (execution slip despite correct understanding). Research demonstrates that "errors followed by corrective feedback produce stronger learning than errorless learning" due to prediction violation deepening encoding. However, each error type requires fundamentally different instructional responses—re-teaching for conceptual errors, guided practice for procedural errors, and metacognitive monitoring for careless mistakes.
Research establishes that errors, properly analyzed rather than simply corrected, become powerful learning mechanisms. Borasi's work demonstrated errors as diagnostic windows into student thinking. Black & Wiliam identified error analysis as central to formative assessment's diagnostic power. Metcalfe's review confirms learning gains from error analysis. Siegler's microgenetic research showed mathematical development depends on understanding why incorrect strategies fail. Tulis et al. modeled productive error processing through three components: error detection, attribution, and correction strategy—all teachable through systematic analysis.
Evidence sources:
Required:
Optional:
Error classification requires working samples, not isolated answers. Diagnostic questioning is essential for accurate classification.
This skill addresses individual errors; whole-class patterns require instructional redesign through class-level diagnostic approaches.
Selective application is necessary given time constraints—use for persistent, surprising, or shared errors rather than comprehensive analysis of every student mistake.
Chains well with: feedback-quality-analyser, gap-analysis-from-student-work, metacognitive-prompt-library, worked-example-fading-designer
Design mastery experience sequences that systematically build student confidence in skills they avoid, using Bandura's self-efficacy theory.
By Gareth Manning
This intervention constructs a structured progression of tasks that methodically develops self-efficacy for learners who hold limiting beliefs about their capabilities in specific areas. Rather than relying on verbal encouragement alone—which research shows is the weakest efficacy intervention—the approach engineers genuine success experiences starting from the student's current competence level and advancing through carefully calibrated increments.
The method leverages Bandura's four efficacy sources in strategic sequence: mastery experiences (actual success at progressively challenging tasks), vicarious experience (observing similar peers succeed), verbal persuasion (credible, evidence-based encouragement), and physiological state management (reducing performance anxiety).
Bandura's research established that "self-efficacy" is a central determinant of motivation and task performance, particularly noting it predicts outcomes even when controlling for actual ability. Hattie's synthesis identified self-efficacy as one of the strongest individual predictors of achievement. The critical insight: self-efficacy develops through accumulated evidence of capability, not reassurance.
Schunk and Pajares demonstrated that learners' confidence beliefs shape academic outcomes independent of actual ability level. Dweck's work on growth mindset complements this framework, though mindset messaging alone proves ineffective without genuine mastery experiences.
Start from demonstrable strength. Identify what the student already does competently that connects to the target skill. First task success should be near-certain.
Increment difficulty minimally. Each subsequent task represents one small step upward—students must perceive the connection between their last success and the new challenge.
Embed attribution coaching. After each success, guide learners to attribute outcomes to their specific strategies and efforts: "You used the planning method we practised" rather than "You're naturally talented."
Manage physiological state. Anxiety undermines self-efficacy. Employ low-pressure conditions, private rather than public performance, and normalise difficulty as evidence of learning.
Apply vicarious experience strategically. Show peers with similar starting points succeeding, not only high achievers, so the message is credible: "If someone like me can do this..."
Avoid generic praise ("You're so clever!"), unsupported encouragement, public comparison to peers, ability-focused language, and extrinsic reward systems. These undermine credibility and can damage self-efficacy if the student later encounters difficulty.