Chapter 8: Recommendations We call for the R&D sector to ensure that product designs are based on best and most current principles of teaching and learning. The first decade of adaptivity in edtech drew upon many important principles, for example, around how to sequence learning experiences and how to give students feedback. And yet the underlying conception was often deficitbased. The system focused on what was wrong with the student and chose pre-existing learning resources that might fix that weakness. Going forward, we must harness AI’s ability to sense and build upon learner strengths. Likewise, the past decade of approaches was individualistic, and yet we know that humans are fundamentally social and that learning is powerfully social. Going forward, we must build on AI capabilities that connect with principles of collaborative and social learning and which respect the student not just for their cognition but also for the whole human skill set. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners. Going forward, we must ensure that AI-enabled learning resources are intentionally inclusive of these students. The field has yet to develop edtech that builds upon each student’s ability to make choices and to self-regulate in increasingly complex environments. We have to develop edtech that expands students’ abilities to learn in creative modes and to expand their ability to discuss, write, present, and lead. We also call upon educators to reject uses of AI that are based solely on machine learning from data—without triangulation based on learning theory and knowledge from practice. Achieving effective and equitable educational systems requires 95 | P a g e
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