‘All or Nothing’ Approach to AI ‘Risks Shutting Down Innovation’

Miriam Schneider, director of learning initiatives at Google DeepMind, argues that banning AI in higher education could stifle innovation in teaching methods and learning design. She emphasizes the need for AI to be intentionally integrated into pedagogy, grounded in learning science, rather than assuming it will naturally enhance education without intervention.
Miriam Schneider, director of learning initiatives at Google DeepMind, warns that outright bans on AI in higher education risk halting discussions about how to innovate teaching practices. She argues AI should reinforce pedagogy rather than replace human elements, offering opportunities to rethink holistic learning models that prioritize connections, motivation, and critical thinking over rote knowledge transfer. Schneider, who joined DeepMind last September after two decades at Google, stresses that AI’s role in education depends on intentional design. She compares expecting large language models (LLMs) to excel in teaching without prior training to hiring an untrained stranger to lead a classroom. Instead, AI must be built around proven learning science principles, such as Google’s LearnLM methodology, which underpins tools like Google Classroom and Gemini’s guided learning mode. The debate follows concerns over AI-driven academic cheating, with tech companies introducing features like ‘study modes’ to encourage deeper engagement. Schneider highlights the need for educators and edtech developers to collaborate, treating learning as a science to optimize AI’s role. At last month’s World Education Forum, she urged stakeholders to ‘bring the outside in,’ ensuring AI aligns with pedagogical best practices rather than disrupting them. While AI can enhance certain aspects of learning—such as personalization or accessibility—Schneider cautions against overestimating its capabilities without structured integration. The ‘all or nothing’ approach risks missing opportunities to evolve education systems, she says, emphasizing that meaningful progress requires balancing technology with human-centered design. Schneider’s perspective aligns with broader discussions about redefining teaching and assessment in the AI era. She advocates for reflective conversations about the future of education, where AI serves as a tool to augment—not replace—human instruction and student growth.
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