Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature. In this paper, we introduce Prompt Coach (PC), an agentic tutor that helps developers learn how to craft high-quality code-generation prompts through Socratic guidance embedded in-flow within their IDE. PC evaluates prompt quality across multiple dimensions and surfaces targeted questions to guide self-correction, grounded in the developer's codebase and the behavior of the target LLM. We present an early empirical study with 15 professional developers combining quantitative prompt quality scoring with qualitative perception measures. Participants showed statistically significant improvements after a single 60-minute session, with the largest gains across dimensions commonly overlooked by developers. They also reported strong trust, high adoption readiness, and unanimous agreement that PC improved their prompt-writing skills.
A simulation-based textual analysis of prompt design evaluates a frontier large language model as a tutor across 60 scripted sessions on a single topic and point to dynamic, dialogue-aware prompting alongside explicit SRL scaffolding.
Kendall Hartley, Fabiola Sáez-Delgado, Javier Mella-Norambuena· Future Internet· 0 citations
The results suggest that the pedagogical behavior of AI tutors may not be easily steered through system prompts alone: embedding established SRL and CE frameworks did not produce detectable improvements on any preregistered outcome in a large, ecologically valid deployment.
Maximilian Georg Barth, Sverrir Thorgeirsson, K. Etemadi et al.· International Computing Educ...· 0 citations
The system attained a System Usability Scale (SUS) score of 88.5 and cut grading time by 87.5 percent, facilitating focused instructor review in LLM-supported programming assessments, facilitating focused instructor review in LLM-supported programming assessments.
A. Ibrahim, Runal Rezkiawan· EDUMATIC: Jurnal Pendidikan...· 0 citations
These findings inform the design of AI-enhanced assessment practices that support fair, transparent, and pedagogically aligned learning environments and highlight the pedagogical implications of prompt sensitivity in AI-assisted grading.
The development and calibration of the COM Essay Assessor is presented, a rubric-based generative artificial intelligence (GenAI) tool designed to support formative feedback while retaining instructor oversight and reflects on the opportunities and challenges of integrating GenAI into large writing programs.
Juhi Bansal· The International Journal of...· 0 citations
This manuscript presents a descriptive study design and preliminary findings from an undergraduate engineering mechanics course conducted in Spring 2026, and details a reproducible survey instrument used to capture student AI usage patterns, attitudes, and verification practices, which are subsequently linked to academ...
S. Geng, Helen Lallos-Harrell, Jiya Ashar et al.· arXiv.org· 0 citations
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