Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Lauren Singelmann, Minnesota State University, Mankato; Jack Elliott, Minnesota State University, Mankato; Yuezhou Wang, Minnesota State University, Mankato; Jacob John Swanson, Minnesota State University, Mankato

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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Capabilities to Perform Specific TasksIntroductionGenerative AI (GAI) tools like ChatGPT and Copilot can quickly prepare polished, fiveparagraph essays and clever limericks about any given topic, but can they multiply seventwo-digit numbers? Or answer a question from the Fundamentals of Engineering exam? Or tellyou what the image in a “connect-the-dots” puzzle is? GAI tools are designed to be able toproduce human-like language responses to given prompts, but performance varies depending onthe nature of each task. To further complicate the evaluation of GAI performance, each tool (e.g.ChatGPT, Copilot, Gemini) has its own process for generating responses, and these processescan evolve rapidly – with success varying across tools

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ibukun Samuel Osunbunmi, Pennsylvania State University; Taiwo Raphael Feyijimi, University of Georgia; Lexy Chiwete Arinze, Purdue University at West Lafayette (COE); Viyon Dansu, Florida International University; Bolaji Ruth Bamidele, Utah State University; Yashin Brijmohan, Utah State University; Stephanie Cutler, The Pennsylvania State University

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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

AI toolsare trained when using the tool. Some questions were posed to encourage critical thinking, such asexamining the data used for AI training, the reliability of AI outputs, and strategies for fine-tuningAI tools. This reflective process aimed to help participants balance human judgment with AIassistance effectively.Furthermore, the participants were introduced to Bloom's taxonomy as a framework for developingAI literacy [13], progressing from foundational knowledge acquisition to the creation of originalwork (See Figure 1). Practical sessions involved the use of resources like ChatGPT, Scholarly,Elicit, and Consensus as AI as a tutor and for aiding literature reviews and syntheses. Similar AIworkshops have been held by the facilitator

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Md. Yunus Naseri, Virginia Polytechnic Institute and State University; Vinod K. Lohani, Virginia Polytechnic Institute and State University; Manoj K Jha P.E., North Carolina A&T State University; Gautam Biswas, Vanderbilt University; Caitlin Snyder; Steven X. Jiang, North Carolina A&T State University; Caroline Benson Sear, Virginia Polytechnic Institute and State University

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Diversity

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

required by employers. As more data and analytical methods becomeavailable, more aspects of the economy, society, and daily life will become dependent on data-driven decision-making. Recognizing this shift, the National Academies of Sciences (2018)emphasizes that academic institutions must prioritize developing "a basic understanding of datascience in all undergraduates" to prepare them for this new era [1]. This is particularly crucial forSTEM graduates, who must develop varying levels of expertise in working with data – the abilityto understand, interpret, and critically evaluate data, as well as to use data effectively to informdecisions. The recent emergence of large language models (LLMs) such as ChatGPT, which arebecoming increasingly