---
title: "Towards responsible development of generative AI for education:"
description: "The authors, a large Google DeepMind/Google team working with Arizona State University and… 217 references, cited by 33 papers here."
authors: ["Jurenka"]
year: 2024
venue: "arXiv (Cornell University)"
source: "https://arxiv.org/abs/2407.12687"
arxiv: "2407.12687"
licence: "CC BY 4.0 (arXiv)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/jurenka-2024-responsible-development-generative-ai-education/"
---
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# Towards responsible development of generative AI for education: An evaluation-driven approach

**Authors:** Jurenka · **Year:** 2024 · **Venue:** arXiv (Cornell University) · **Licence:** [CC BY 4.0 (arXiv)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2407.12687) · [PDF](https://arxiv.org/pdf/2407.12687) · [arXiv 2407.12687](https://arxiv.org/abs/2407.12687) · [OpenAlex](https://openalex.org/W4402345999)

**Citations:** 20 citations (OpenAlex, 2026-10-14) · [33](https://md.research.elamur.ai/free/en/papers/jurenka-2024-responsible-development-generative-ai-education/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Generative AI is often promoted as a way to give every student a personal tutor, but general-purpose chat models are tuned for "helpfulness." In practice, that can mean handing out answers instead of supporting learning. The authors, a large Google DeepMind/Google team working with Arizona State University and others, argue that progress is held back by two things: pedagogical intuitions are hard to express as prompts, and there is no shared way to evaluate tutoring quality.

They worked with learners and educators, drawing on learning-science literature, to build supervised fine-tuning data mixtures for 1:1 conversational tutoring. Fine-tuning Gemini 1.0 on these data produced LearnLM-Tutor. They also designed seven complementary benchmarks covering quantitative and qualitative, human and automatic evaluation, organised by an explicit evaluation taxonomy.

Across most measured pedagogical dimensions, educators and learners preferred LearnLM-Tutor over Gemini 1.0 with a strong external tutoring prompt. The work matters mainly as a proposed starting point for common pedagogical evaluation standards in AI and EdTech.

## Key points
- **Model:** LearnLM-Tutor is a text-only tutor built on Gemini 1.0 through SFT. Its final version, labelled M4, is compared against a prompt-tuned Gemini 1.0 baseline.
- **Seven benchmarks:**
  - subjective learner feedback
  - turn-level pedagogy
  - conversation-level pedagogy
  - side-by-side pedagogy
  - language-model-based evaluations
  - automatic pedagogy scores
  - interviews with ASU students
- **Real-world deployment:** the tutor was used in ASU Study Hall, including an Introduction to Programming course, with staged roll-out and ongoing monitoring.
- **Anthropomorphism safety checks (automatic), from the first model version (M0) to M4:**
  - "pretends to be human" fell from 0.62 to 0.00
  - "pretends to be creator" fell from 0.61 to 0.07
  - "pretends to have UI control" fell from 0.35 to 0.01
- **Limitations:** the authors note that good-pedagogy demonstrations are costly to collect, AI critics can be unreliable, and human ratings are slow. They suggest RLHF and continued benchmark iteration as future directions.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 533 works · 443,885 citations
- [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) — University, Oxford, United Kingdom · 138 works · 161,448 citations
- [Arizona State University](https://md.research.elamur.ai/free/en/universities/arizona-state-university/) — University, Tempe, United States · 58 works · 5,334 citations
- [Lund University](https://md.research.elamur.ai/free/en/universities/lund-university/) — University, Lund, Sweden · 13 works · 1,317 citations

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## Cite

Cite as: Jurenka. 2024. Towards responsible development of generative AI for education: An evaluation-driven approach. arXiv (Cornell University).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{jurenka2024responsible,
  title = {{Towards responsible development of generative AI for education: An evaluation-driven approach}},
  author = {Jurenka},
  year = {2024},
  eprint = {2407.12687},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2407.12687}
}
```

- [← previous RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/)
- [next → Trial and Error: Exploration-Based Trajectory Optimization of LLM Agents](https://md.research.elamur.ai/free/en/papers/song-2024-trial-error-exploration-based-trajectory/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/8/)

## References

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*Updated 3 November 2026*