Conference Agenda

This proposal advances an expanded conceptual framework for understanding postsecondary student engagement, learning outcomes, and personal development in the age of Artificial Intelligence (AI). Building on Pascarella’s (1985) causal model of student development and Weidman’s (1989) undergraduate socialization model, the proposed framework positions AI as a new and influential socialization agent embedded within students’ academic and social environments. As AI tools such as generative large language models increasingly mediate how students learn, interact, and make meaning of their educational experiences, traditional frameworks that focus primarily on human and institutional influences risk underestimating a critical dimension of contemporary student experience (Hu, 2026).

Pascarella’s causal model emphasizes the role of precollege characteristics, institutional structures, peer and faculty interactions, and the quality of student effort in shaping learning and developmental outcomes. Similarly, Weidman’s socialization model highlights how normative contexts, interpersonal relationships, and socializing agents shape students’ values, attitudes, and behaviors over time. While both models have been foundational in higher education research, neither explicitly accounts for the pervasive presence of non-human, intelligent agents that increasingly provide cognitive, academic, and even socio-emotional support to students.

The expanded framework proposed here conceptualizes AI as an additional environmental and socializing force that interacts dynamically with existing elements in Pascarella’s and Weidman’s models. AI tools influence the quality and nature of student engagement by shaping how students allocate cognitive effort, engage in self-regulated learning, seek help, and interact with peers and faculty. Empirical evidence suggests that AI can enhance personalization and access to learning support, yet it can also contribute to reduced metacognitive engagement, altered peer interaction patterns, and shifts in students’ sense of belonging and agency (Hu, 2026). These effects have direct implications for student learning and personal development.

By integrating AI into established theoretical models, this proposal calls for a more comprehensive understanding of student experiences and outcomes in contemporary higher education. Treating AI as a socialization agent allows researchers and practitioners to better capture how student learning and development are shaped by interactions not only with people and institutions, but also with intelligent technologies that increasingly mediate those interactions. The proposed expanded framework provides a foundation for future empirical research and evidence-informed policy and practice aimed at maximizing the benefits of AI while safeguarding the core developmental purposes of higher education.

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Generative artificial intelligence (AI) is accelerating transitions at the intersection of science, education, and society, raising pressing questions about trust in higher education: trust in academic work, in assessment, and in the credibility of qualifications. For university teachers, these challenges translate into new professional demands—supporting students’ learning under AI-mediated conditions, safeguarding scientific standards, and maintaining assessment legitimacy when AI use is widespread and difficult to verify. While current research offers numerous models of AI literacy and technology-enhanced teaching, higher-education-specific frameworks that conceptualise AI teaching competence as a distinct professional construct remain underdeveloped.

This paper presents a conceptual competence model for AI Teaching Competence in Higher Education developed through a multi-step, theory-guided design. Building on a systematic synthesis of existing AI literacy and AI-related teaching frameworks, the model consolidates teaching-specific competence areas and extends them to reflect competence demands highlighted in current debates. These include AI-aware assessment, supervision of academic work, motivating learners beyond AI automation, and fostering scientific practice and integrity in the age of AI – competences that are central to sustaining trust and supporting students’ academic experience in times of transition. By conceptualising AI teaching competence as a normative and epistemic professional capacity, the model positions higher education teaching as a key mechanism for learning in transition. The paper discusses the theoretical contribution of the model and outlines implications for future empirical validation, teacher education, and educational policy.