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Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M. and Kagal, L. (2018) Explaining Explanations: An Overview of Interpretability of Machine Learning. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, 1-3 October 2018, 80-89.
https://doi.org/10.1109/dsaa.2018.00018

has been cited by the following article:

• TITLE: Integrating Generative AI in Engineering Education: Interaction Dynamics and Pedagogical Implications

  AUTHORS: Stefano Filippi

  KEYWORDS: Generative AI, Engineering Education, Human-AI Interaction, Product Representation, Agency, Design Cognition

  JOURNAL NAME: Intelligent Information Management, Vol.18 No.3, May 11, 2026

  ABSTRACT: Generative Artificial Intelligence (AI) is rapidly transforming engineering education, particularly in activities involving product representation and conceptual design. While existing literature has extensively addressed students’ perceptions and the technical capabilities of AI systems, comparatively less attention has been devoted to the interaction processes that emerge during real educational tasks. This paper adopts a conceptual and educational perspective to examine how students interact with AI tools in design-oriented contexts. Building on insights from Human-Computer Interaction and design cognition, the study identifies recurring interaction tendencies and analyzes them through key dimensions such as goal formulation, interpretation of outputs, and distribution of agency between user and system. Rather than proposing a formal framework, the paper develops an extended conceptual discussion aimed at supporting pedagogical reflection, and outlines implications for teaching practices that promote reflective engagement and meaningful learning.