TeachLM: Optimizing LLMs for Education

• TeachLM is a specialized language model framework that harnesses authentic tutoring dialogues and parameter-efficient fine-tuning to deliver adaptive, human-like instructional performance.
• The system employs rigorous privacy-preserving data curation from over 100,000 hours of real-world tutoring and utilizes LoRA to target pedagogically salient model dimensions.
• Synthetic dialogue generation and multi-turn evaluation reveal measurable gains, including doubled student talk time and 50% increased session depth, aligning outputs closer to human tutoring.

TeachLM is a LLM system and research direction explicitly focused on optimizing LLMs for educational use through post-training with authentic, longitudinal student–tutor interaction data and rigorous parameter-efficient adaptation. The central aim is to bridge the gap between generic, compliance-driven LLM outputs and the nuanced, adaptive pedagogical strategies required for effective one-on-one tutoring. Developed as a response to the limitations of prompt engineering and traditional LLM pretraining, TeachLM leverages a massive dataset of real-world tutoring dialogues and introduces specialized fine-tuning and evaluation protocols to achieve human-like instructional performance.

1. Motivation and Objective

TeachLM addresses intrinsic pedagogical deficiencies in mainstream LLMs, which are typically trained to minimize completion loss or maximize compliance (“helpfulness”) in isolated, short-form tasks. In authentic educational contexts, effective tutoring often involves dialogic friction—eliciting background knowledge, countering misconceptions, calibrating autonomy, and iteratively adapting instructional strategies to student signals over prolonged, interactive sessions. The TeachLM design philosophy targets these deficiencies by grounding model behavior in real, diverse, and longitudinal student–tutor interactions, shifting instructional optimization from “giving the answer” to modeling the multifaceted dynamics of expert human tutors (Perczel et al., 6 Oct 2025).

2. Authentic Learning Data: Scale and Curation

The TeachLM system is constructed atop an extensive and uniquely authentic dataset comprising over 100,000 hours of one-on-one longitudinal student-tutor sessions sourced from the Polygence platform. The coverage spans a wide curriculum spectrum—from STEM problem-solving to project-driven humanities instruction—and incorporates relationship dynamics (such as rapport development and iterative background assessment) over months of student progress. Data curation involved a rigorous privacy-preserving pipeline: removal of all personal information, reframing of platform-specific elements, and securing explicit user consent prior to session recording. These datapoints are canonicalized into dialogue pairs and skill “landmarks” for robust supervised fine-tuning without compromising student or tutor privacy (Perczel et al., 6 Oct 2025).

3. Parameter-Efficient Fine-Tuning and Model Adaptation

TeachLM employs parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA), to adapt baseline state-of-the-art models (including Gemini 2.5 Flash and GPT-4) for tutoring use. LoRA inserts low-rank matrices into frozen model layers, enabling targeted adaptation with significantly less computational resource compared to full model retraining. TeachLM fine-tuning is conducted in supervised multi-epoch cycles over anonymized educational dialogues, optimizing for conversational structure (e.g., increased student participation, improved questioning dynamics) and pedagogical behaviors empirically observed in the dataset. By constraining weight updates to pedagogically salient dimensions, TeachLM preserves general model capabilities while significantly increasing educational alignment (Perczel et al., 6 Oct 2025).

4. Synthetic Dialogue Generation and Multi-Turn Evaluation

A distinctive feature of TeachLM’s research and product methodology is the use of high-fidelity synthetic student–tutor dialogues for scalable evaluation. Using a separately fine-tuned “authentic student model,” TeachLM simulates multi-turn dialogue sessions in which the model’s instructional performance can be benchmarked without requiring constant human raters. This method supports dialogical diagnostics: metrics such as student talk time, words per tutor turn, average questions per turn, and turn count to session closure are recorded and compared against human–human tutoring baselines. An automated judge LLM determines stopping conditions or conversation wrap-up, and repeated simulations (typically 100 per evaluation point) afford statistically robust comparisons (Perczel et al., 6 Oct 2025).

Metric	Human–Human Reference	Pre-Tune LLM	Post-Tune (TeachLM)
Student talk time (%)	~30	5–15	~30
Tutor words/turn	72	150–300	~72
Questions/interrogative turn	1–2	3–4	1–2
# Dialogue turns/convo	—	Baseline	+50%

5. Measured Improvements and Behavioral Shifts

Synthesized and empirical evaluations consistently demonstrate that parameter-efficient fine-tuning on authentic tutoring data yields substantial improvements over baseline LLMs:

• Student talk time nearly doubles, converging to human–tutor norms and reflecting increased elicitation and student-centeredness.
• Tutor verbosity decreases: average tutor response length falls from excessively long outputs (150–300 words) to concise human-aligned utterances (~72 words).
• The questioning strategy improves: interrogative turns now contain 1–2 focused questions, compared to 3–4 per turn in non-educationally tuned models, reducing cognitive overload.
• Average session length (measured in dialogue turns) increases by ~50%, indicating deeper, more iterative instructional processes.
• Personalization metrics, including the frequency and specificity of questions aimed at uncovering student background or skill level, also show significant gains post-tuning (Perczel et al., 6 Oct 2025).

6. Implications, Pedagogical Validity, and Future Directions

TeachLM’s paradigm has significant implications for personalized, scalable education:

• Generalization: The approach demonstrates that authentic, domain-specific educational datasets can be leveraged to post-train general-purpose LLMs for high-fidelity, adaptive instruction without full model retraining.
• Scalability: Synthetic dialogue generation and robust benchmarking enable rapid model iteration and ablation of pedagogical strategies without constant reliance on human-in-the-loop evaluation.
• Ethical Use and Privacy: The model’s pipeline demonstrates that privacy-preserving, consent-based educational data collection can be done at scale, supporting future work in secure AI-driven educational platforms.

Future work cited includes reinforcement learning from human feedback (RLHF) to further align pedagogical nuances, integration of direct student-in-the-loop evaluations during live learning journeys, and expansion to multimodal and multilingual educational contexts. Notably, the authors emphasize that parameter-efficient approaches are essential to maintaining tractability and rapid deployment in both research and production scenarios (Perczel et al., 6 Oct 2025).

7. Broader Connections and Position in Educational LLM Research

TeachLM’s methodology aligns with broader movements in educational LLM research, such as the use of longitudinal classroom simulations (Zhang et al., 27 Jun 2024), instructional alignment with LoRA and retrieval augmentation (Shojaei et al., 11 Apr 2025), and reinforcement-learning-based pedagogical alignment (Dinucu-Jianu et al., 21 May 2025). Its contribution is distinguished by the unprecedented scale and authenticity of the dialogue corpus, rigorous attention to privacy, and the demonstration that practical fine-tuning can elicit verifiable shifts toward effective, dialogic AI tutoring.

The system establishes a new standard for educational LLM benchmarking and development, evidenced by measurable, reproducible gains in core instructional metrics across dialogical, pedagogical, and personalization dimensions. This positions TeachLM as a critical step toward robust, scalable, and human-aligned AI-driven instruction in future educational technologies.