Zawacki-Richter et al.’s systematic review highlights a major gap in AI research applied to higher education: educators are often absent from leadership roles and key decision-making processes. The paper points out that computer scientists predominantly lead AI projects, leaving pedagogical perspectives sidelined. This absence raises concerns about whether AI tools truly address educational needs or merely serve technical interests (Zawacki-Richter et al. 12).
Holstein et al. explore AI systems designed to provide "orchestration support," helping teachers manage classroom activities, monitor student engagement, and provide timely interventions. Their research shows how AI can augment teachers’ ability to personalize learning and maintain classroom flow, but only when educators are involved in design to ensure the tools align with real teaching workflows (Holstein et al. 6).
Rose Luckin emphasizes the necessity of ethical, educator-guided AI assessment tools. She argues that AI systems evaluating student performance must be transparent, unbiased, and reflect teachers’ professional judgment. Her 2017 article and subsequent podcast interviews emphasize that without educators’ input, AI assessments risk misrepresenting student abilities and reinforcing inequalities (Luckin, "Towards Artificial Intelligence-Based Assessment Systems"; Luckin, "The Role of Educators in AI-Enhanced Learning").
Neil Selwyn’s book critiques the notion that AI can replace teachers. He underscores the irreplaceable value of human relationships, ethical considerations, and the complexities of teaching that AI cannot replicate. His work advocates for a human-in-the-loop approach, where AI is a tool under educator oversight rather than a substitute (Selwyn).
Chen et al. review the current landscape of AI in education and identify a lack of grounding in educational theory for many tools. This gap results in AI applications that may be efficient technically but lack pedagogical soundness, further illustrating the need for educator involvement in the development process (Chen et al. 75270).
Roll and Wylie trace the evolution of AI in education and emphasize that the next revolution in AI’s educational utility depends heavily on embedding educators in design and decision-making. Their work offers a hopeful outlook if collaboration deepens (Roll and Wylie 584).
Darrell West from the Brookings Institution underscores the importance of policy frameworks that incorporate ethical AI design principles, including meaningful educator input to ensure AI tools are equitable, transparent, and effective (West).
Klein’s EdSurge article provides concrete examples of EdTech products that faltered due to lack of teacher involvement. These failures highlight the tangible consequences of excluding educators from the design and implementation of AI tools (Klein).