@inproceedings{chen-etal-2026-digitalcoach,
title = "{D}igital{C}oach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching",
author = "Chen, Meng and
Ji, Anya and
Wu, Tsung-Han and
Maringgele, Tobias and
Chan, David M. and
Suhr, Alane and
Pavel, Amy",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.770/",
pages = "17868--17907",
ISBN = "979-8-89176-459-0",
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 consisting of 22,752 dialogue turns grounded in 28.1 hours of screen and input event recordings across 5 software applications. We then use DigitalCoach to evaluate whether state-of-the-art models can teach humans how to use computers. Our automated evaluation shows that models differ from humans in how they coach: models provide more direct instructions, but fewer explanations, error diagnoses, and knowledge-check questions. When we fix the coaching method, models produce utterances that are similar to human references, but poorly grounded in visual context. Our interactive evaluation confirms that model coaches cause learners to passively follow instructions without deeper engagement and fall short in visual grounding. DigitalCoach lays a foundation for collaborative and proactive computer use coaching agents. Data and code are available at https://project-digital-coach.vercel.app"
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<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 consisting of 22,752 dialogue turns grounded in 28.1 hours of screen and input event recordings across 5 software applications. We then use DigitalCoach to evaluate whether state-of-the-art models can teach humans how to use computers. Our automated evaluation shows that models differ from humans in how they coach: models provide more direct instructions, but fewer explanations, error diagnoses, and knowledge-check questions. When we fix the coaching method, models produce utterances that are similar to human references, but poorly grounded in visual context. Our interactive evaluation confirms that model coaches cause learners to passively follow instructions without deeper engagement and fall short in visual grounding. DigitalCoach lays a foundation for collaborative and proactive computer use coaching agents. Data and code are available at https://project-digital-coach.vercel.app</abstract>
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%0 Conference Proceedings
%T DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching
%A Chen, Meng
%A Ji, Anya
%A Wu, Tsung-Han
%A Maringgele, Tobias
%A Chan, David M.
%A Suhr, Alane
%A Pavel, Amy
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-digitalcoach
%X 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 consisting of 22,752 dialogue turns grounded in 28.1 hours of screen and input event recordings across 5 software applications. We then use DigitalCoach to evaluate whether state-of-the-art models can teach humans how to use computers. Our automated evaluation shows that models differ from humans in how they coach: models provide more direct instructions, but fewer explanations, error diagnoses, and knowledge-check questions. When we fix the coaching method, models produce utterances that are similar to human references, but poorly grounded in visual context. Our interactive evaluation confirms that model coaches cause learners to passively follow instructions without deeper engagement and fall short in visual grounding. DigitalCoach lays a foundation for collaborative and proactive computer use coaching agents. Data and code are available at https://project-digital-coach.vercel.app
%U https://aclanthology.org/2026.emnlp-main.770/
%P 17868-17907
Markdown (Informal)
[DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching](https://aclanthology.org/2026.emnlp-main.770/) (Chen et al., EMNLP 2026)
ACL