Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering

TL;DR

This work addresses bias in AI-assisted engineering advising by developing a bias-aware chatbot for the University of Maryland's A. James Clark School of Engineering. Using prompt engineering, data curation, retrieval-augmented generation, and regex-based bias detection, the authors build a program-specific advisor with 75 evaluated prompts. The results show high accuracy, relevance, and personalization (9.76, 9.56, 9.60 respectively) and a 0% bias rate under tested conditions, though the small sample size limits generalizability. The study provides actionable best practices for ethical AI in higher education and outlines future enhancements such as vector-search-based retrieval and broader user testing to further ensure equitable advising outcomes.

Abstract

Selecting a college major is a difficult decision for many incoming freshmen. Traditional academic advising is often hindered by long wait times, intimidating environments, and limited personalization. AI Chatbots present an opportunity to address these challenges. However, AI systems also have the potential to generate biased responses, prejudices related to race, gender, socioeconomic status, and disability. These biases risk turning away potential students and undermining reliability of AI systems. This study aims to develop a University of Maryland (UMD) A. James Clark School of Engineering Program-specific AI chatbot. Our research team analyzed and mitigated potential biases in the responses. Through testing the chatbot on diverse student queries, the responses are scored on metrics of accuracy, relevance, personalization, and bias presence. The results demonstrate that with careful prompt engineering and bias mitigation strategies, AI chatbots can provide high-quality, unbiased academic advising support, achieving mean scores of 9.76 for accuracy, 9.56 for relevance, and 9.60 for personalization with no stereotypical biases found in the sample data. However, due to the small sample size and limited timeframe, our AI model may not fully reflect the nuances of student queries in engineering academic advising. Regardless, these findings will inform best practices for building ethical AI systems in higher education, offering tools to complement traditional advising and address the inequities faced by many underrepresented and first-generation college students.

Figures (6)

• Figure 1: Flowchart demonstrating workflow methodology for developing the bias-aware AI chatbot following the methodology subsection structure.
• Figure 2: Gradio interface V1 of Chatbot showing the user interaction panel and evaluation tools.
• Figure 3: Score Frequency Distributions by Prompt Category for Temperature = 0.7
• Figure 4: Histogram of scores for 75 prompts at Temperature = 0.0
• Figure 5: V2 Commercial Model interface design for the AI chatbot showing enhanced user experience features and improved layout.