Educators Embrace AI Training to Enhance Teaching Methods

Artificial intelligence (AI) is significantly changing how students engage with learning, from writing essays to practicing languages, leaving educators scrambling to adapt. As schools and universities navigate this rapid transformation, the question of how both students and teachers are learning to utilize AI effectively becomes increasingly relevant.

Currently, much of this learning occurs informally. Students exchange advice on platforms like TikTok and Discord, while teachers share insights in staff rooms or through professional networks such as LinkedIn. Although these informal channels facilitate knowledge sharing, they often lack a critical examination of essential issues like bias, surveillance, and equity. This gap highlights the urgent need for structured teacher education that focuses on the ethical implications of AI in education.

According to a recent study, educators are generally unprepared for the complexities introduced by AI technologies. Many lack the necessary skills to assess the reliability and ethics of AI tools. Professional development programs frequently focus on technical training without addressing the broader implications of AI usage. This uncritical application risks perpetuating existing biases and inequities within educational environments.

In response to this challenge, Johanathan Woodworth developed a professional development module as part of a graduate-level course at Mount Saint Vincent University. This course engaged teacher candidates in hands-on exploration of AI tools for feedback and plagiarism detection, collaborative design of assessments that integrate AI, and case analyses of ethical dilemmas in multilingual classrooms. The overarching goal was to shift from casual experimentation to a more critical engagement with AI.

During the sessions, participants displayed enthusiasm for AI and reported a newfound ability to evaluate tools critically, recognize bias, and apply AI thoughtfully in pedagogical contexts. By the end of the course, candidates confidently used terminology like “algorithmic bias” and “informed consent,” framing AI literacy as a professional judgment closely linked to their teaching identities.

These classroom observations reflect broader institutional challenges regarding AI policy in education. Universities across the globe have adopted fragmented approaches to AI: some implement outright bans, while others cautiously endorse its use, and many maintain vague stances. This inconsistency breeds confusion and mistrust among educators.

Alongside colleague Emily Ballantyne, Woodworth examined how AI policy frameworks can be adapted for Canadian higher education. Faculty acknowledged the potential of AI while expressing concerns about equity, academic integrity, and the impact on workload. They proposed a model introducing a “relational and affective” dimension, emphasizing that AI influences trust and dynamics in teaching relationships.

When institutions fail to set clear policies, individual educators often find themselves acting as ad hoc ethicists, lacking institutional support. Clear and practical policies are necessary, but they must also be accompanied by a commitment to building knowledge and practices that promote critical use of AI. Teacher education programs must prioritize AI literacy, integrating it into curricula and outcomes instead of relegating it to optional workshops.

Candidates expressed a need for clarity on institutional expectations regarding AI. Institutions should clearly distinguish between inappropriate uses, such as ghostwriting, and valid applications like feedback support, as recommended by recent research.

Furthermore, the evolution of AI knowledge requires ongoing support through learning communities. Faculty circles, curated repositories, and interdisciplinary hubs can facilitate the sharing of strategies and the discussion of ethical dilemmas. Equity considerations must also be at the forefront, particularly as AI tools often embed biases that disadvantage multilingual learners. Institutions should conduct equity audits and align AI adoption with accessibility standards to ensure fair outcomes for all students.

Public debates surrounding AI in educational settings often oscillate between excitement for innovation and fear of academic dishonesty. This binary perspective fails to capture the complexities of how students and teachers are truly learning to harness AI. While informal learning networks are valuable, they often do not provide the necessary framework for ethical reasoning.

Structured opportunities for teachers to explore AI can transform them from passive adopters into active shapers of technology. This transformation is crucial for ensuring that educators are not merely reacting to technological changes but actively directing how AI supports equity, pedagogy, and student learning.

To truly harness the potential of AI in education, education systems must nurture this agency, ensuring that AI serves to enhance, rather than undermine, the learning experience.