AI Coaches Struggle with Explanations and Visual Grounding in Software Training
Summary
A new multimodal dataset, DigitalCoach, reveals that state-of-the-art AI models, when coaching humans on computer use, provide more direct instructions but fewer explanations, error diagnoses, or knowledge checks than human experts. Models also struggle with visual grounding, leading to passive learning.
Why it matters
Professionals developing AI-powered educational tools or internal training systems need to understand these limitations to design more effective and engaging learning experiences that go beyond mere instruction.
How to implement this in your domain
- 1Integrate multimodal input (screen recordings, user actions) into AI coaching systems to improve visual grounding.
- 2Develop AI models with explicit objectives for explanation generation, error diagnosis, and knowledge assessment.
- 3Design interactive learning modules that encourage active engagement rather than passive instruction following.
- 4Conduct user studies to compare AI-led coaching effectiveness against human-led coaching for specific software tasks.
Who benefits
Key takeaways
- Current AI coaches prioritize direct instructions over explanations and error diagnosis.
- AI models struggle with visual grounding in real-time computer use coaching.
- Learners tend to be passive when coached by current AI systems.
- Future AI coaching agents need improved pedagogical strategies and multimodal understanding.
Original post by Meng Chen, Anya Ji, Tsung-Han Wu, Tobias Maringgele, David M. Chan, Alane Suhr, Amy Pavel
"arXiv:2606.31980v1 Announce Type: cross Abstract: Agents are increasingly capable of automating software tasks, but can they teach humans how to use software themselves? We introduce DigitalCoach, a multimodal dataset of 72 human expert-novice computer use coaching sessions consi…"
View on XOriginally posted by Meng Chen, Anya Ji, Tsung-Han Wu, Tobias Maringgele, David M. Chan, Alane Suhr, Amy Pavel on X · view source
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