Trustworthiness, Accuracy, and Quality in Artificial Intelligence Contribution Assessment: A Conceptual Framework for Evaluating AI–Human Authorship in Written Documents

The rapid adoption of generative artificial intelligence (GenAI) has fundamentally transformed academic, scientific, and professional writing by enabling increasingly sophisticated collaboration between humans and intelligent computational systems. Although numerous AI contribution assessment technologies have emerged to distinguish AI-generated from human-written text, existing approaches remain primarily focused on computational detection of linguistic patterns and provide limited evidence regarding intellectual contribution, measurement validity, transparency, or overall trustworthiness. Consequently, the theoretical foundations required to evaluate AI contribution assessment systems remain underdeveloped, limiting their suitability for high-stakes educational, scholarly, organisational, and regulatory decision-making.
The current study addresses the conceptual gap by developing the Human–Artificial Intelligence Contribution Assessment Trustworthiness Index (HAICATI), an interdisciplinary theory-building framework for evaluating the trustworthiness of AI contribution assessment systems. Drawing upon Information Quality Theory, Trust Theory, Attribution Theory, Measurement Theory, Human–Computer Interaction Theory, the Technology Acceptance Model, and Responsible Artificial Intelligence Governance, the framework conceptualises trustworthy AI contribution assessment as a second-order reflective latent construct represented by eight theoretically derived dimensions: Detection Accuracy, Attribution Accuracy, Reliability, Transparency, Explainability, Human Contribution Recognition, Contextual Understanding, and Measurement Validity. Rather than proposing another AI detection algorithm, HAICATI provides a multidimensional framework for evaluating whether AI contribution assessment systems are scientifically credible, transparent, contextually appropriate, and ethically responsible evidence capable of supporting informed human decision-making.
The principal contribution of this study is the reconceptualisation of AI contribution assessment from a computational classification problem to a multidimensional theory of trustworthy assessment that integrates psychometric measurement, behavioural science, information systems, organisational governance, and responsible artificial intelligence. By providing a coherent theoretical foundation for future scale development, empirical validation, independent benchmarking, and international standardisation, HAICATI advances the emerging literature on trustworthy AI and establishes a foundation for the responsible evaluation and governance of collaborative human–AI authorship.

Generative artificial intelligence; AI contribution assessment; AI authorship; trustworthiness

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