2026 ASEE Annual Conference & Exposition

Objective and Motivations: Preparing the next-generation workforce to thrive in a digital and artificial intelligence (AI)-driven world is essential for both individual success and for sustaining U.S. leadership in global innovation. Yet, despite the rising demand for data and AI expertise, many higher education institutions lack interdisciplinary, hands-on learning opportunities—particularly affecting first-generation students with limited exposure to data-centric technologies. To bridge this gap, this project designed and evaluated a 15-week experiential learning course designed to promote data literacy, academic motivation, and interdisciplinary applications in emerging technologies. We investigated two research questions (RQs): RQ1: How does participation in a 15-week experiential learning course enhance students’ motivation and confidence in applying data analytics to emerging technologies? RQ2: What are students’ perceptions of the benefits and challenges of pursuing data-intensive careers after completing the course?

Methods: The course included eight modules: six technical modules on statistics, database management with SQL, and AI fundamentals using Google Colaboratory (a free, cloud-based Jupyter notebook environment for Python) and three modules on project management, oral and written presentation, and leadership to strengthen students’ soft skills. The study was conducted at a university in the Southwestern United States, where undergraduate students were recruited though a dedicated online recruiting network, emails, and flyers. Students qualified to participate if they were in good academic standing and received a $4000 tuition stipend for participating. Participants (N=23) reported their gender as 57% female, 43% male, ethnicity as 52% Hispanic, and race as 52% White, 30% Asian, 9% American Indian/Alaska Native, 4% Black, 4% multiracial. Most were STEM majors (74%), primarily third- and fourth-year students (86%), with 13% international students. The mean age was 21 years. Students (N=23 pretest; N=13 posttest) completed matched surveys measuring data literacy efficacy, interest in emerging technology, and open-ended questions about perceived benefits and challenges of pursuing careers in emerging technology. Each survey was adapted from existing literature.

Evaluation and Results: All analytic measures demonstrated strong reliability (Cronbach’s α > .88). For RQ1, results showed significant improvements in students’ interest and data-literacy from pretest to posttest. A linear mixed-effects model revealed gains in personal STEM interest (d=.772, p=.014; Mpre=6.1, Mpost=6.7, Maximum=7) and data-literacy self-efficacy (d=2.19, p=.012, Mpre=2.6, Mpost=4.0, Maximum=5), with other variables showing non-significant positive trends. For RQ2, analysis of open-ended responses revealed perceived challenges including a competitive job market (36%), gender and racial bias in STEM (36%), the need to constantly improve their knowledge (36%), and difficulty of content (18%). Reported benefits included opportunities to develop skills (74%), potential to improve the lives of others (36%), and financial rewards (27%). This study demonstrates that an experiential, interdisciplinary data analytics curriculum can effectively enhance students’ motivation, confidence, and career readiness for AI-driven fields, particularly supporting broader participation in the emerging technology workforce.

Plan of Submission and Focus: This study focuses on the development of an experiential learning opportunity to prepare people for data-centric careers in emerging technology. We have documented student learning and performed all data analysis. We anticipate completing the full manuscript by the close of 2025 and preparing it for submission to ASEE by January 2026.