AI in Academia: A Critical Look at the Promise of AI

This artifact documents my participation in a panel session titled AI in Academia: A Critical Look at the Promise of AI, featuring scholars and practitioners exploring the implications of generative AI in higher education. Topics included personalized learning, workforce transformation, curriculum integration, and the risks of automation. Panelists addressed potential harms, such as AI’s role in widening social gaps, and emphasized the uncertainty, complexity, and ethical stakes of integrating these tools into academic practice.


Core Competencies

  • Integrating Inclusive Design Principles and Practices

    This session challenged me to think about inclusivity not just in terms of learner access or interface accessibility, but in terms of whose epistemologies, labor, and futures are centered—or ignored—by AI systems. Panelists explored the risks of algorithmic bias, surveillance, and extractive data practices that disproportionately affect marginalized learners and educators. These insights reframed inclusive design as an infrastructural commitment, not just a course-level concern.

  • Using Research and Evaluation Skills

    The panel emphasized the importance of critical, research-informed engagement with AI in education. Panelists encouraged educators to ask evidence-based questions about effectiveness, risk, and unintended consequences. This session reinforced the need for rigorous evaluation when integrating AI into pedagogy and institutional systems.

This session helped me pause and reflect on the broader systems that shape educational technology. It reminded me that design decisions are never neutral—they carry ethical weight and social consequences. I left the panel thinking more critically about how technologies are framed, who is centered in those narratives, and what it means to design not just for efficiency or novelty, but for equity and care.