[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85352-en":3,"doc-seo-85352-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85352,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Uncovering Students’ Mental Models of Generative Artificial Intelligence","Study of undergraduate students’ mental models of generative AI (GenAI) and how these internal frameworks shape perceptions of capabilities and constraints, as well as choices about integrating GenAI into academic work. The paper asks which mental models students hold and which forms of conceptual knowledge—declarative, procedural, and conditional—appear in those models. Using concept maps from 64 students, structured coding identifies five model categories. Declarative knowledge dominates, indicating surface-level understanding and limited procedural and conditional reasoning. Findings inform AI literacy via curriculum and guidelines for responsible, ethical use.","Uncovering Students’ Mental Models of Generative  \nArtificial Intelligence  \nAmrita Ganguly  \nInformation Sciences and Technology George Mason University Fairfax, VA, USA [agangul@gmu.edu](agangul@gmu.edu)  \nSai Sharanya Garika Information Sciences and Technology George Mason University Fairfax, VA, USA [sgarika@gmu.edu](sgarika@gmu.edu)  \nAditya Johri  \nInformation Sciences and Technology George Mason University Fairfax, VA, USA [johri@gmu.edu](johri@gmu.edu)  \nAbstract—In this paper we present a study of students’ mental models of generative AI (GenAI). A student’s mental model of GenAI influences not only how they perceive the technology’s capabilities and limitations but also how they choose to integrate it into their academic work. Whether they view it as a collaborative partner, a shortcut to complete tasks, or something in between, depends on how they conceptualize its use. This study addresses the following questions: (I) What mental models do undergraduate students hold about GenAI? and (II) What aspects of conceptual knowledge - declarative, procedural, and conditional - are present in these mental models? Sixty-four concept maps were collected from students enrolled in a course on technology ethics. Students were asked to construct concept maps representing their understanding of GenAI use. The concept maps were analyzed using a structured codebook and the analysis revealed five categories of mental models: technical process based, educational tool based, transition model, consequence aware model, integrated model. Declarative knowledge was most dominant across maps, suggesting that students largely understood GenAI primarily at a surface level - knowing its names, tools, and applications but demonstrate limited procedural understanding of how it works and limited conditional knowledge about when and why it should or should not be used. By identifying students’ mental models, we can improve students’ AI literacy by designing curriculum and guidelines that improve cognition while ensuring responsible and ethical use.  \nKeywords—Undergraduate students, mental models, concept maps.  \nI. INTRODUCTION  \nMental models are internal cognitive frameworks used to reason and make decisions and can be the basis of individual behaviors with a system. They are constructed by individuals based on their unique life experiences, perceptions, and understandings of the world [1] . In the context of educational technology use, mental models are significant because they fundamentally shape how learners understand new concepts, make decisions about technology use, and develop behavioral patterns around emerging tools. According to Carroll & Olson [2] mental models indicate utility over accuracy and effectiveness.  \nGenAI applications such as ChatGPT, Claude, and GitHub Copilot are rapidly being integrated into higher education, adding a new dimension to how students think about and use AI. Students actively engage with GenAI for academic purposes, including improving assignment drafts, generating ideas, and  \ncompleting tasks. Such use can improve short-term task performance, for example, by increasing essay scores [3,4] . Discrepancies between students’ mental models and their use though can lead to significant problems including misunderstandings about appropriate use, unintentional policy violations, missed opportunities for productive engagement with technology, or resistance to institutional directives that seem disconnected from students’ understanding of GenAI.  \nPrior research on GenAI adoption in education has focused on quantitative assessments of knowledge, measuring what students know about AI concepts through tests and surveys, or examining patterns of tool adoption and technical skill development. While valuable, these approaches provide an incomplete picture because they rarely investigate the underlying cognitive structures, reasoning processes, and intuitive frameworks that students use to make sense of GenAI. Exploring ","cbCaiqCYiUTxJ0fc","https://ap.wps.com/l/cbCaiqCYiUTxJ0fc","pdf",393742,3,1,5,"English","en",105,"# Introduction\n## Mental models in education technology\n## GenAI use in higher education\n## Prior research limitations\n## Concept maps as expressed models\n# Study Overview\n## Purpose and research questions\n## Method: concept mapping and analysis","[{\"question\":\"What questions does the study aim to answer about GenAI mental models?\",\"answer\":\"The study asks what mental models undergraduate students hold about GenAI and what aspects of conceptual knowledge (declarative, procedural, and conditional) are present in those models.\"},{\"question\":\"How was students’ understanding of GenAI elicited and analyzed?\",\"answer\":\"The study collected 64 concept maps from students in a technology ethics course, then analyzed them using a structured codebook to categorize the mental models.\"},{\"question\":\"What did the analysis reveal about the kind of knowledge students used most?\",\"answer\":\"Declarative knowledge was most dominant, suggesting students largely understood GenAI at a surface level while showing limited procedural understanding and limited conditional knowledge about when and why to use it 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