How to be an Educator: Navigating Identity and Power in an Era of AI
As Artificial Intelligence becomes increasingly embedded in higher education, how can we ensure that educators remain at the heart of teaching and learning? In a thought-provoking guest talk at the TUM Center for Educational Technologies, Prof. Allison Littlejohn from University College London (UCL) explored how AI is transforming educators’ professional identities, agency, and responsibilities. Drawing on research into professional learning and digital transformation, she highlighted the opportunities AI brings to education, while raising important questions about human judgment, invisible workloads, and shifting power dynamics.
Teacher Agency: Why Human Judgment Still Matters
A central theme of the talk was teacher agency: educators’ ability to make informed decisions and shape their teaching practices. While AI can assist with lesson planning, analyze student engagement, and provide personalized feedback, it cannot replace the relational and ethical dimensions of teaching.
Drawing on research conducted during the COVID-19 pandemic, Prof. Littlejohn described how educators struggled with “teaching into a void” when online environments limited their ability to understand students’ reactions and build meaningful relationships.
These challenges remain relevant in the age of AI. An algorithm might identify a student as disengaged, but only an educator can interpret the broader context and decide whether to encourage, challenge, or support them. AI agentically interacts with learners, but educators remain responsible for interpreting learner responses. Using human agency educators act with care, fairness, and professional judgment.
The Hidden Work Behind AI: Understanding Meta-work
Although AI promises to reduce educators’ workloads, Prof. Littlejohn introduced the concept of meta-work, the often invisible work required to make other work possible.
For educators, this can involve checking AI-generated lesson plans, interpreting learning analytics, monitoring automated feedback, or redesigning assessments to preserve academic integrity. Rather than simply eliminating tasks, AI introduces new responsibilities that require time, expertise, and continuous learning.
However, meta-work also creates opportunities for professional development. By critically evaluating AI-generated outputs and adapting them to students’ needs, educators can strengthen their pedagogical expertise.
The challenge is that this additional work often goes unrecognized. As AI becomes more deeply integrated into educational systems, institutions must acknowledge these responsibilities rather than assuming that technological efficiency automatically translates into reduced workloads.
The Hidden Work Behind AI: Understanding Meta-work
Another important aspect of the talk concerned the impact of technological transformation on professional identity.
Drawing on research into changing professional practices, Prof. Littlejohn highlighted vulnerabilities such as fear of losing control, concerns about recognition, erosion of trust, and unequal access to resources. When technologies automate aspects of professional work, individuals may begin to question their expertise and their role.
For educators, this raises a fundamental concern: Could teaching increasingly become a matter of managing AI systems rather than engaging directly with students?
Prof. Littlejohn also emphasized the shifting power dynamics surrounding educational technology. As universities adopt AI platforms, technology providers and developers may gain greater influence over educational decisions, potentially moving authority away from educators and their pedagogical expertise.
A Call to Action: Observe, Learn, and Adapt
Rather than rejecting AI, Prof. Littlejohn argued for a more thoughtful, collaborative approach to technological change.
She presented an iterative framework built around five steps: identifying cultural challenges, reframing problems, co-designing pathways, facilitating collaborative learning, and evaluating impact. This approach recognizes that successful digital transformation requires more than technical training. It also demands attention to professional cultures, institutional structures, and the experiences of those working with new technologies.
The talk concluded with a reminder that the future of education should not be determined by technological capabilities alone. As AI reshapes how we teach and learn, protecting educators’ professional agency, recognizing their evolving work, and preserving meaningful human relationships must remain central priorities.
Ultimately, the question is not simply how educators can adapt to AI, but how AI can be shaped to support the educators we want and need in the future.
Missed the talk? Stay tuned for more EdTech events!
