The rise of generative artificial intelligence (AI) is prompting higher education systems to re-examine long-standing pedagogical assumptions, particularly in the domain of student assessment. This paper explores the implications of AI for assessment practices in teacher education programs, where traditional formats such as research papers, seminar projects, and final essays are increasingly vulnerable to AI-generated content. These developments raise ethical and professional concerns regarding the authenticity of student work and the validity of assessment outcomes.

A growing response among faculty has been to revert to in-class exams and other forms of summative testing as a means of control. However, this shift is often driven by mistrust and fear of academic dishonesty rather than pedagogical deliberation. Such a reactive approach undermines the broader educational mission of teacher preparation, which should aim to cultivate reflection, collaboration, resilience, and ethical judgment—competencies that are not easily measurable through conventional exams.

Instead of treating AI solely as a threat, we argue that the current moment offers an opportunity to rethink the very purpose and design of assessment in higher education. Contemporary critiques have shown that dominant models of assessment, rooted in Enlightenment rationalism and neoliberal logics of accountability, tend to overemphasize final outcomes at the expense of learning processes. This paradigm encourages students to prioritize achievement over engagement, results over reflection, and individual performance over collective inquiry.

In response, we advocate for formative, process-oriented, and holistic assessment models. These include structured checkpoints during the semester, reflective journaling, collaborative work, and self- and peer-assessment tools that highlight the student’s journey, not just their product. In teacher education, such practices not only better reflect the complexity of professional learning but also offer students ethical and pedagogical tools they can later implement in their own classrooms.

Importantly, AI can be integrated constructively within this paradigm. When used transparently, AI tools can support formative feedback, stimulate critical thinking, and open discussions on ethical, social, and methodological questions. Students can be asked to explain how they used AI, reflect on its limitations, and demonstrate learning beyond what machines can produce.

While these approaches may require more engagement from instructors, they have the potential to restore trust between faculty and students, shift the culture from surveillance to dialogue, and promote deeper learning. For teacher education, this shift is both timely and necessary, not only to adapt to technological change but also to model the kind of reflective, ethical, and socially conscious pedagogy that future educators must embody.

Keywords: Artificial Intelligence, AI, Education, Assessment, Higher Education, Teacher Education.