HESHAM AHMED

Hesham Ahmed

Hesham Ahmed

Doctoral Researcher

Email: [email protected]

Hesham focuses on the intersection of AI alignment in education, automated feedback, and idiographic learning analytics. I aim to develop an application that supports students’ self-regulation by providing individualized, aligned, and automated feedback using large language models (LLMs).

Idiographic Learning Analytics (LA) shifts the focus of data analysis from the “average student” (nomothetic) to the “individual student” (idiographic). Traditional analytics often compare a student against a class-wide norm, but idiographic LA treats each learner (N=1) as a case study. This approach involves collecting data from a single individual using sources such as learning management systems, sensors, and questionnaires. By analyzing the data, through novel methods such as Transition Network Analysis, we are able to gain understanding of self-regulation patterns and temporal learning habits.

AI Alignment in Education: Given the increasing integration of AI in education and the intertwined nature of human needs and requirements, it is important to assess the alignment of AI technology with ethical, educational, and usability aspects to continue reaping its benefits while mitigating its detriments. AI Alignment in Education refers to the challenge of ensuring that artificial intelligence tools align with the educational and ethical values and pedagogical goals. There are possible harms of AI misalignment such as misinformation—where AI produces text that is fabricated and presents it as a fact, damaging the trust of the users and hence, the possibility of further relying on it. Moreover, there are ethical concerns about the privacy of students’ data, in addition to possible harms coming from bias.

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Publications

  1. Ahmed, H., Kayaduman, H., López-Pernas, S., Tukiainen, M., & Saqr, M. (2025). User-centric Evaluation of GenAI Alignment and Recommendations based on Predictive Learning Analytics. In Proceedings of the 2nd Workshop on Generative AI for Learning Analytics (GenAI-LA).
  2. López-Pernas, S., Belayachi, I., Ahmed, H., Elmoazen, R., & Saqr, M. (2024). Augmenting AI with curated learning analytics literature: building and initial exploration of a local RAG for supporting teachers (LARAG). In CEUR Workshop Proc.