Development and Preliminary Validation of the ARROW-AI Framework for Responsible AI-Assisted Research Writing in Higher Education
Keywords:
academic integrity; generative artificial intelligence; higher education; research writing; responsible AI useAbstract
Generative artificial intelligence is increasingly used in higher-education research writing, yet institutions often lack operational procedures to protect authorship, ensure research alignment, verify, and disclose. This study developed and preliminarily evaluated the ARROW-AI Framework and Toolkit through a sequential multiphase developmental-validation design with embedded mixed-methods formative evaluation and a bounded pilot. Participants comprised 12 experts, 84 student-researchers, 18 advisers/instructors, and 32 pilot cases. Descriptive expert-domain ratings ranged from 3.38 to 3.58 for the framework and from 3.20 to 3.53 for the toolkit, while all 15 components met the prespecified I-CVI and CVR criteria. Student-researcher domain ratings ranged from 4.05 to 4.18, and adviser/instructor ratings ranged from 3.82 to 4.24; documentation and workload burden were moderate. Pilot completion averaged 88.91%; six initially high-risk requests were down-scoped, and four cases required substantive revision. Because the instruments covered conceptually distinct formative domains, ratings were interpreted descriptively by domain rather than as scores from unidimensional psychometric scales. Integrated evidence supported six revisions, including shorter records, completed examples, clearer risk decisions, and stronger adviser guidance. ARROW-AI should therefore be treated as a developmental process guide rather than evidence of effectiveness or misconduct prevention.
https://doi.org/10.26803/ijlter.25.8.37
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