While LLMs are increasingly used as teaching assistants, their teaching capability remains insufficiently evaluated — a critical gap in current AIED research.

TeachBench: Evaluating LLM Teaching Ability

Syllabus-grounded framework for measuring LLM teaching capability via student performance improvement after multi-turn instruction.

The Gap in LLM Evaluation

Li et al. (2026) address a critical gap: while LLMs are increasingly used as teaching assistants, their teaching capability remains insufficiently evaluated.

Limitations of Existing Benchmarks

Benchmark Type	Focus	Limitation
Problem-solving (MMLU, HELM, GSM8K)	Answer correctness	Measures solver, not teacher
Exam-centric (AGIEval, C-Eval, GAOKAO-Bench)	Exam performance	Still solution-centric
Tutoring dialogues (MathDial, TutorBench)	Per-turn response quality	Misses end-to-end teaching effectiveness

TeachBench shifts the evaluation target: from solving to teaching.

Syllabus-Grounded Framework

Core Design Principles

1. Knowledge-centered: Evaluation based on structured knowledge points (syllabus), not target questions

2. Leakage-controlled: Teacher agents restricted to knowledge points + example problems (no access to test items)

3. Outcome-based: Teaching effectiveness measured by student agent's performance improvement

Workflow

```

Syllabus → Knowledge Tree → Teacher Agent (multi-turn instruction) → Student Agent → Performance Gain

```

Key Findings from Gaokao Experiments

Using Chinese National College Entrance Examination (Gaokao) data across multiple subjects:

Finding	Implication
Domain variation: Math teaching effective (7.63pt gain with Qwen3-235B), but physics/chemistry challenging	Teaching ability is domain-specific, not generalized
Example problems backfire: Models shift to error correction vs. syllabus-grounded instruction	Current LLMs struggle with structured teaching vs. reactive problem-solving
Teaching ≠ Solving: Models good at solving aren't necessarily good at teaching	Teaching ability is a distinct LLM behavior dimension

Connection to Existing Work

vs. AI Tutor Effectiveness

Traditional ITS effectiveness reviews focus on human learning outcomes with deployed systems

TeachBench evaluates model teaching capability in controlled agentic settings

Both highlight: teaching is more than problem-solving

vs. Educational LLM Alignment

Alignment benchmarks measure: "Does this model produce good teaching content?"

TeachBench measures: "Does this model improve learning through instruction?"

Complementary: alignment → content quality; TeachBench → instructional effectiveness

vs. Agentic Workflows

TeachBench operationalizes the "teacher agent" paradigm in agentic education

Reveals current LLMs struggle with structured pedagogical planning (vs. reactive Q&A)

Aligns with: agentic reflection, planning, and tool use in educational contexts

Implications for AI in Education

For LLM Development

Teaching ability as benchmark dimension: Should be evaluated alongside reasoning, coding, etc.

Domain-specific training: Models need pedagogy-grounded training, not just subject knowledge

Structured vs. reactive: TeachBench exposes the gap between knowing and teaching

For ITS Design

Syllabus grounding: Effective teaching requires structured knowledge (cf. quantum ITS knowledge graph)

Multi-turn instruction: Teaching effectiveness emerges over sequences of interactions, not single turns

Evaluation rigor: ITS should be evaluated on learning gains, not just user satisfaction

For Benchmark Design

Leakage control: Don't give models the test questions they're supposed to teach

Student proxies: Fixed-capability student agents enable reproducible evaluation

Outcome metrics: Performance improvement > response quality scores

Connected Concepts

Self Regulated Learning

AI Tutoring

Prompt Engineering

Adaptive Learning

Connected Articles

AI Tutor Effectiveness Review

Educational LLM Alignment

Agentic Workflows Education

Quantum Education ITS

Citation

Models, T.A.S.F.F.E.T.A.I.L.L. (2026). TeachBench - Evaluating LLM Teaching Ability