Abstract:

Generative artificial intelligence (GenAI) is increasingly present in higher education, raising important questions regarding its integration into assessment processes. Despite this growing presence, there is still limited evidence about lecturers’ readiness to engage with generative AI in assessment, including their levels of knowledge, current use and perceptions. In practice, teaching staff often display highly heterogeneous positions, ranging from restrictive or cautious approaches to more active or intensive use, which makes it difficult to design coherent and well-targeted institutional responses.

Within this context, this contribution presents the design of a diagnostic survey specifically developed to assess lecturers’ readiness to integrate generative artificial intelligence into assessment practices in higher education, including attitudes and perceived challenges. The instrument has been developed in the context of the EvaluIA project and is intended to support informed pedagogical and innovation-oriented decision-making.

Methodology and Results:
The survey is structured around six key dimensions: lecturers’ knowledge of GenAI, current use in assessment-related tasks, perceptions of usefulness, perceived barriers and risks, expectations regarding educational value, and willingness to integrate GenAI-assisted tools into assessment processes. The survey has been designed to enable the analysis of how lecturers’ perceptions, confidence and attitudes towards the use of generative AI in assessment vary across different academic and personal profiles, such as disciplinary background, teaching context, instructional level or professional experience.
The design of the instrument draws on relevant existing surveys and prior studies on technology adoption and assessment practices in higher education, adapted to the specific context of generative AI. While the instrument is conceived as a general and adaptable tool for higher education contexts, it is currently applied within two engineering schools at the Universitat Politècnica de València: the Higher Technical School of Industrial Engineering and the Higher Technical School of Computer Engineering.

Conclusions:
This communication presents a methodological contribution focused on the development and justification of a diagnostic instrument. The proposed survey represents a structured, transferable and institutionally relevant tool that can be adopted by higher education institutions to diagnose lecturers’ readiness, inform assessment-related innovation strategies and support evidence-informed decision-making in the context of generative artificial intelligence.

Acknowledgement:
This research is funded by the Universitat Politècnica de València through the Educational Innovation and Improvement Project PIME/25-26/536.

Keywords:

generative artificial intelligence, assessment, higher education, lecturer readiness, diagnostic survey.