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I am a Postdoctoral Research Associate in the Department of Mechanical Engineering at Imperial, working on automated feedback with the Lambda Feedback Team. I completed my PhD at King’s College London, where my research focused on Automated Assessment of Code Quality. Prior to that, I obtained my MSci in Computer Science from Royal Holloway, University of London.

My research focuses on automated assessment and feedback, student engagement and curricula alignment with industry expectations.

Before my PhD, I worked as a full-stack software engineer within a data analytics company and was responsible for various projects, including data ingestion and web-based data visualization.

Research Topics

  • Automated Feedback
  • Automated Assessment
  • STEM Education
  • Computer Science Education
  • Software Tools
  • Human-Computer Interaction

Key Projects

Menagerie: A Dataset of Graded CS1 Assignments

The Menagerie dataset consists of a second semester CS1 assignment that ran over four academic years (18/19 - 21/22). It consists of 667 total submissions, with 273 of those being subsequently graded post hoc as part of a study into the consistency of human graders, and includes final grades and feedback for correctness, code elegance, readability and documentation.

OpenScienceFoundation

Lambda Feedback: Automated Formative Feedback at Imperial

Since 2025, I have been developing and researching automated formative feedback on the Lambda Feedback platform at Imperial College London, combining GenAI and traditional approaches to provide feedback to over 4,000 students annually. This includes leading a project on a shared API standard for educational microservices with colleagues at TU Munich, ETH Zürich, and Nanyang Technological University.

Lambda Feedback

PEAF: Pedagogical Evaluation of Automated Feedback

I co-founded and lead PEAF, the First International Workshop on Pedagogical Evaluation of Automated Feedback, held as part of AIED 2026’s Festival of Learning. The workshop brings together researchers and practitioners to develop shared methods for evaluating whether automated feedback tools actually support student learning, rather than judging them on technical performance alone.

Website · Paper

Grants

  • College Teaching Fund - King’s College London - £9,222.50 (2023)
  • College Teaching Fund (Travel/Dissemination Grant) - King’s College London - £1,000 (2024)
  • Imperial Global Connect Fund - Imperial - £5,950 (2025)

Publications

Journal Articles

  1. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2025). How Consistent Are Humans When Grading Programming Assignments? ACM Trans. Comput. Educ., 25(4), 1–37. https://doi.org/10.1145/3759256
  2. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2024). Automated Grading and Feedback Tools for Programming Education: A Systematic Review. ACM Trans. Comput. Educ., 24(1). https://doi.org/10.1145/3636515

Conference Articles

  1. Su, X., Song, Y., Messer, M., Savelka, J., Cutumisu, M., & Wang, A. (2025). Can GPT4 Generate Effective Feedback on Code Readability? Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2, 773. https://doi.org/10.1145/3724389.3730771
  2. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2025). Menagerie: A Dataset of Graded Programming Assignments. Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2, 1547–1548. https://doi.org/10.1145/3641555.3705129
  3. Messer, M., Shi, M., Brown, N. C. C., & Kölling, M. (2024). Grading Documentation with Machine Learning. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial Intelligence in Education (pp. 105–117). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64302-6_8
  4. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2023). Machine Learning-Based Automated Grading and Feedback Tools for Programming: A Meta-Analysis. Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, 491–497. https://doi.org/10.1145/3587102.3588822
  5. Messer, M. (2022). Grading Programming Assignments with an Automated Grading and Feedback Assistant. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (pp. 35–40). Springer International Publishing.
  6. Messer, M. (2022). Detecting When a Learner Requires Assistance with Programming and Delivering a Useful Hint. In A. Mitrovic & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (pp. 778–781). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6852958
  7. Messer, M. (2022). Automated Grading and Feedback of Programming Assignments. Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2, 638–639. https://doi.org/10.1145/3502717.3532113
  8. Brown, N. C. C., Messer, M., & Ikin, J. (2025, November). Failures in Reliably Assessing Program Code Readability. Proceedings of the 25th Koli Calling International Conference on Computing Education Research. https://doi.org/10.1145/3769994.3770017
  9. Johnson, P., Neagu, A., Messer, M., Lundengard, K., & Ramsden, P. (2025). How Do We Define and Evaluate "Good" Automated Feedback? Proceedings of the SEFI 53rd Annual Conference. https://doi.org/10.5281/zenodo.17631687
  10. Clear, A., Impagliazzo, J., Fokum, D., Kazmi, Z., Messer, M., Pereira, T., Polash, M., Pow-Sang, J. A., Thomas, M., & Zhang, M. (2025). Exploring the Effectiveness of Computing Curricular Recommendations. Proceedings of the 2025 on ACM Conference on Global Computing Education Vol 2. https://doi.org/10.1145/3736251.3749525
  11. Neagu, A., Wong, J. T. H., Messer, M., Nelson, R., & Johnson, P. B. (2026). Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments. ICML 2026 Workshop on Pluralistic Alignment. https://arxiv.org/abs/2606.15766
  12. Sölch, M., Neagu, A., Messer, M., Johnson, P., Kortemeyer, G., Ng, S. S. H., Lim, F. S., & Krusche, S. (2026). μEd API: Towards A Shared API for EdTech Microservices. Proceedings of the Eleventh ACM Conference on Learning @ Scale. https://doi.org/10.1145/3774398.3811592
  13. Messer, M., Johnson, P., Neagu, A., Kandiko Howson, C., Savelka, J., & Woodhead, S. (2026). The First International Workshop on Pedagogical Evaluation of Automated Feedback (PEAF 2026). AIED 2026: Festival of Learning Workshops and Tutorials. https://doi.org/10.1007/978-3-032-29794-5_10
  14. Neagu, A., Johnson, P., Messer, M., & Lim, F. S. (2026). Build and Deploy Microservices for Automated Feedback. AIED 2026: Festival of Learning Workshops and Tutorials. https://doi.org/10.1007/978-3-032-29794-5_13

Miscellaneous

  1. Neagu, A., Messer, M., Johnson, P., & Nelson, R. (2026). "How Do I ...?": Procedural Questions Predominate Student-LLM Chatbot Conversations. arXiv. https://arxiv.org/abs/2602.18372
  2. Johnson, P., Ramsden, P., & Messer, M. (2025). AI Microservices for Sustainable Innovation in Education. EdArXiv. https://doi.org/10.35542/osf.io/wq4bd_v1