Reconstructing Faculty Evaluation in Higher Vocational Education in the Age of Artificial Intelligence: Value Foundations, Institutional Mechanisms, and Technological Boundaries
Abstract
artificial intelligence. More information, however, does not settle the question of what counts as good evaluation. That problem is acute
in higher vocational education, where important work often takes place in practical teaching, cooperation with industry, and other settings
that cannot be reduced easily to uniform indicators. The central issue is therefore institutional rather than purely technical: which forms of
faculty work are worth recognizing, how evidence is interpreted, how results are used, and how far AI is allowed to enter consequential decisions. This article examines those questions through the value basis of evaluation, the rules by which judgments are made, and the limits
of technological participation. It argues for criteria tied more closely to vocational education, for results that feed into faculty development,
and for AI to remain an aid to evidence handling rather than the final judge. Four areas of reform follow from this argument: differentiated
standards, evaluation based on actual work settings, interpretable and auditable AI-supported systems, and development-oriented use of
evaluation findings.
Keywords
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DOI: http://dx.doi.org/10.70711/neet.v4i10.10308
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