Responsible Generative AI Use in Thesis Work: The ALIGN Framework and Checklist for Students and Supervisors

Abstract

Generative artificial intelligence is increasingly built into how students prepare, write, and revise bachelor's and master's theses, yet the principles meant to govern responsible use were largely written for research outputs rather than for supervised, assessed, and educational student work. This study asks what responsible AI use requires in the thesis specifically, and develops a framework for it. Guidance on responsible AI use already exists for research - including the eight-principle consolidation of Knöchel et al. (2025): regulations, data security, quality control, originality, bias mitigation, accountability, transparency, and broader impact - but they were written for published outputs, not supervised and assessed student work. Using these established principles as one structured starting point rather than a template to apply, I conducted 28 semi-structured interviews with current students, recent graduates, lecturers and supervisors, program managers, and domain experts, analyzed with Template Analysis, and let the evidence confirm, reshape, and extend them. Quality control and accountability became load-bearing; originality, transparency, and broader impact required substantial reinterpretation; data security and bias mitigation remained normatively important despite limited spontaneous salience. The evidence further pointed to a stake the research framework does not contain: the integrity of the learning the thesis is designed to develop and certify. I define this ninth principle, learning integrity, as alignment between intended thesis learning outcomes, the activities meaningfully performed by the student, and the evidence used to assess them. The resulting ALIGN framework combines nine principles with prospective agreement, process-based supervision, proportionate disclosure, verification, and dialogic defense - a triangulated integrity architecture for use before, during, and at the end of thesis work, rather than any single instrument treated as proof of authorship. The paper closes with a one-page student checklist and a citable disclosure sentence for thesis methods sections. The study contributes to education research and higher education assessment by specifying how generative AI changes the relation between student agency, self-regulated learning, evidence of competence, and academic integrity in capstone thesis work.