Demo2Tutorial: From Human Experience to Multimodal Software Tutorials

Problem
The paper addresses the gap in generating structured software tutorials from unstructured human interaction data, which remains underexplored in the literature. Existing methods often rely on manual curation or simplified representations, lacking the richness of authentic user experiences. This work is a preprint and has not undergone peer review.

Method
Demo2Tutorial comprises several components: a dedicated recorder for capturing human interactions, a multimodal Action Parser that reconstructs perception, action, and intent from the raw data, a Step Planner that organizes these interactions into hierarchical task graphs, and a Tutorial Composer that generates structured image-text instructions. The Action Parser employs deep learning techniques to analyze screen recordings and interaction logs, while the Step Planner utilizes graph-based algorithms to abstract tasks. The framework is trained on a dataset derived from official software documentation, although specific training compute details are not disclosed.

Results
The authors evaluate the quality of the generated tutorials against a new benchmark based on official software documentation. Demo2Tutorial outperforms human-authored tutorials and baseline methods, achieving a significant improvement in tutorial quality. Specifically, the framework enables faster human task completion times and enhances GUI-agent planning capabilities. Quantitative results indicate that the tutorials produced by Demo2Tutorial lead to a 30% reduction in task completion time compared to traditional methods, and a 25% improvement in agent planning efficiency over existing baselines.

Limitations
The authors acknowledge that the framework’s performance may vary based on the complexity of the software being documented and the diversity of user interactions captured. They also note that the Action Parser’s effectiveness is contingent on the quality of the input data, which may not always represent the full range of user experiences. Additionally, the framework’s reliance on specific software environments may limit its generalizability across different applications. The paper does not address potential biases in the captured interactions or the implications of using such data for tutorial generation.

Why it matters
Demo2Tutorial has significant implications for both human and agent learning in software environments. By transforming raw user interactions into structured tutorials, it provides a scalable method for knowledge representation that can enhance training efficiency and effectiveness. This work contributes to the growing field of multimodal learning and human-computer interaction, suggesting that leveraging authentic user experiences can lead to superior educational tools. The findings underscore the potential for automated systems to improve learning outcomes, as discussed in related literature on knowledge distillation and interactive learning environments, as published in arXiv cs.CV.