Enhancing higher education faculty competencies in the age of generative AI: Integrating explainability, contestability, and reflective practice
Abstract
The rapid emergence of generative artificial intelligence (AI) and large language models (LLMs) has created both unprecedented opportunities and significant challenges in higher education. While these technologies promise to enhance teaching effectiveness and research productivity, their black-box nature, tendency toward hallucinations, and opacity raise critical concerns about trust, accountability, and pedagogical integrity. This paper addresses the urgent need for a comprehensive framework to enhance faculty competencies in leveraging generative AI responsibly and effectively. We propose TECTRA (Trust through Explainability, Contestability, and Reflective Application), a novel human-centered framework that integrates Explainable AI (XAI) and Contestable AI (CAI) as foundational mechanisms for trustworthy AI adoption in education. The framework is structured around four interdependent pillars: (1) Ethical Grounding, enabled by XAI's transparency; (2) Pedagogical Integration, activated through CAI’s dialogic structure; (3) Technical Literacy, developed through XAI’s interpretable explanations; and (4) Reflective Practice, sustained through combined feedback loops from both mechanisms. We detail specific, measurable faculty competencies mapped to each pillar and provide concrete development activities and tools. Furthermore, we present a phased implementation strategy roadmap spanning assessment, capacity building, and sustainable scaling, alongside comprehensive policy recommendations that emphasize flexibility, transparency, human oversight, and ethical principles. By positioning XAI and CAI as active, functional elements rather than separate technical considerations, TECTRA transforms generative AI from an opaque tool into a transparent, contestable partner for critical inquiry, ultimately fostering enhanced faculty competencies that are adaptive, evidence-based, and ethically grounded in an increasingly AI-driven educational landscape.