Generative AI-assisted coding may have introduced a meaningful opportunity for educational researchers with no programming experience to extend their work beyond typical scholarly outputs by creating functional software. Translating research into applications has historically required significant coding expertise, but generative AI has lowered this barrier substantially. Proposed is educational research-driven development (ERDD), an emergent conceptual cross-disciplinary framework combining the educational research process with the software development lifecycle (SDLC) to produce executable scholarship deployable directly to stakeholders. A cross-disciplinary process comparison revealed similarities and differences between the two fields, producing a unified set of scholar-coder workflow steps and two researcher postures: the Sequential Posture, which concludes research before development begins, and the Integrated Posture, which runs both processes in deliberate tandem. Importantly, ERDD has not been validated with rigorous research, and its generalizability has yet to be established. An applied workflow example is demonstrated from the perspective of a non-technical scholar-coder producing a functional web application for educators to record student mental health observational data. The application is accessible at: https://mlittrell-ttu.github.io/mlittrell.github.io/mwb-tracker.html
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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