Keyword: generative artificial intelligence

ABSTRACT: Artificial intelligence (AI), particularly generative AI, changes assessment by altering the evidential relationship among student work, learning, and professional judgment. Teachers must design tasks when students can readily obtain machine-generated responses, interpret automated feedback and analytics whose validity is conditional, and justify consequential decisions under uncertainty. This critical integrative review synthesizes scholarship on AI in education, assessment, teacher learning, and emerging professional-development interventions. It proposes Assessment-AI Knowledge (A-AIK) as an assessment-specific configuration of professional knowledge rather than a replacement for assessment literacy, AI literacy, or technological pedagogical content knowledge. A-AIK comprises three interdependent capacities: design competence, which establishes conditions for eliciting meaningful evidence; interpretive competence, which calibrates the meaning, uncertainty, and evidential weight of AI-mediated information; and justificatory competence, which supports transparent, fair, and reviewable decisions. Disciplinary authenticity, human agency, equity, transparency and data governance, and epistemic humility operate as cross-cutting commitments. The reviewed literature suggests that promising interventions use scenarios, authentic assessment redesign, structured critique, peer deliberation, and iterative classroom trials. Recurrent limitations include small samples, reliance on self-report, short duration, and measures of confidence or acceptance rather than demonstrated assessment performance. A four-phase professional-learning model—calibrate, redesign, enact, and justify, is proposed as a synthesis-informed design hypothesis requiring empirical evaluation. The article concludes with provisional performance indicators, institutional conditions, and a research agenda for responsible AI-mediated assessment.

ABSTRACT: This study highlights that understanding how faculty adopt technology requires integrated theoretical frameworks rather than the single-theory models often seen in current research. Faculty responses to disruptive technologies, such as generative AI (GenAI), involve complex psychological processes that are frequently overlooked by traditional models. To address this, we developed the Mediated Message Model (MMM) by combining communication theory, behavioral prediction, and motivational psychology, targeting four gaps: fragmented focus, lack of contextual sensitivity, limited process understanding, and constraints. We utilized this framework to design and evaluate a faculty development program featuring a book club format, involving fifty-six faculty members across two cohorts during the 2024–2025 academic year. Data from surveys (n = 30), interviews (n = 6), and action plans (n = 28) supported our predictions, demonstrating that faculty responses depend on interactions between perceived efficacy and value, rather than solely on individual psychological factors. Our analysis identified four distinct cognitive-behavioral outcomes—engaged adoption, impassive acceptance, discouraged hesitation, and aversive rejection—that stem from specific efficacy-value combinations. Faculty members needed multiple stimuli—such as personal experiences, peer demonstrations, and authoritative readings—to effectively adopt GenAI, as no single approach was sufficient. The study also revealed goal orientation patterns indicating that intrinsic versus extrinsic motivation influences technology integration, opening avenues for future research. The MMM advances both theory and practice by aiding faculty development leaders in designing comprehensive, evidence-based strategies that consider the psychological complexity involved in the adoption of GenAI.