Great analogy of LMS as the “Golden Gateway” for school implementations. I could this being problematic since deimplementing and switching LMS is such a challenge, schools/districts might find themselves stuck with an LMS that’s infiltrated by an LLM they’re not comfortable being exposed to. They could turn it off (in theory), but then be paying for a different LLM that’s outside of their environment.
While these AI study modes from OpenAI, Anthropic, and Google represent impressive technological advances, your article underscores something crucial: there's often a significant gap between EdTech adoption and actual learning outcomes. The fact that these tools are "way too quick to respond before asking the 'why' behind the question" and "talk too darn much" suggests we're still prioritizing technological capabilities over pedagogical effectiveness.
What strikes me most is your observation that success still "hinges on whether teachers embrace these tools or resist them." This points to a fundamental challenge - we're building increasingly sophisticated AI tutors without adequately addressing the systemic issues that make technology implementation in education so complex.
I'm curious about your take: How do we ensure that this new wave of AI innovation drives real gains for students rather than just creating more impressive demos? What metrics should we be tracking beyond engagement and adoption rates to measure genuine learning impact?
The multimodal future you predict is exciting, but I'd love to hear your thoughts on how we can build these tools with learning science at the center, not just as an afterthought.
Great analogy of LMS as the “Golden Gateway” for school implementations. I could this being problematic since deimplementing and switching LMS is such a challenge, schools/districts might find themselves stuck with an LMS that’s infiltrated by an LLM they’re not comfortable being exposed to. They could turn it off (in theory), but then be paying for a different LLM that’s outside of their environment.
What do you think about this?
Fascinating analysis on the AI study modes battleground! This really highlights a critical tension in EdTech that we explore in depth at https://1000software.substack.com/p/technology-wont-save-schools
While these AI study modes from OpenAI, Anthropic, and Google represent impressive technological advances, your article underscores something crucial: there's often a significant gap between EdTech adoption and actual learning outcomes. The fact that these tools are "way too quick to respond before asking the 'why' behind the question" and "talk too darn much" suggests we're still prioritizing technological capabilities over pedagogical effectiveness.
What strikes me most is your observation that success still "hinges on whether teachers embrace these tools or resist them." This points to a fundamental challenge - we're building increasingly sophisticated AI tutors without adequately addressing the systemic issues that make technology implementation in education so complex.
I'm curious about your take: How do we ensure that this new wave of AI innovation drives real gains for students rather than just creating more impressive demos? What metrics should we be tracking beyond engagement and adoption rates to measure genuine learning impact?
The multimodal future you predict is exciting, but I'd love to hear your thoughts on how we can build these tools with learning science at the center, not just as an afterthought.