Equity Engine

EquityEngine is a multi-phase research initiative aimed at enhancing the educational experience for engineering students, particularly those with attention disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD), in post-secondary education. By leveraging the transformative potential of artificial intelligence (AI) and adopting a participatory design approach, this research focuses on developing a multifunctional AI-driven platform that provide tailored support and enhances focus, motivation, self-agency, and educational outcomes for engineering students.

Background and Motivation

Existing literatures reveals a pronounced scarcity of AI-driven learning tools developed specifically for post-secondary STEM education. The majority of existing tools are designed primarily for K-12 students, overlooking the distinct, more advanced challenges faced by SLWD in higher education.

Targeted Population

ADHD is the most common condition associated with attention disorders, affecting 21.8% of the SLWD population and up to 13.8% of college students reported in Spring 2024. In addition, students with major depressive disorder also face substantial concentration challenges. Besides those with formal diagnoses, maintaining focus and motivation has been a common challenge among post-secondary engineering students.

Spring 2012

6.7%

Spring 2024

13.8%

Methodology

Mixed-Methods Approach

The mixed-methods approach serves as the primary strategy guiding our user studies, helping us understand user needs and establishing the foundation for our co-design sessions.

Integration of the participatory design and iterative design approach

Iterative design and participatory design will be the main methodologies guiding our design process. We will use co-design sessions to identify evidence-based design principles together with our targeted users and educators. Subsequently, we will create a low-fidelity paper prototype based on these principles and utilize heuristic evaluation alongside usability testing to gather comprehensive feedback and insights from our participants. This feedback will be used to refine the paper prototype before the next iteration of the design.

Proposed Solution

Three-dimensional real-time data will be measured to guide our Adaptive Intervention Module:

Physiological Signals (e.g., heart-rate, eye-tracking);
Behavioral Signals (e.g., click frequency, tab-switching, facial expression);
Contextual Signals (e.g., assignment difficulty, pop-up responses).

Based on these real-time measures, the system will generate an Engagement Index. This index is then fed into a tiered decision logic that determines the interventions (e.g., micro-prompts, task adjustment, contextual re-engagement).

Publications

Kevin Shao, et al. (2025). Bridging Educational Equity Gaps: A Systematic Review of AI-Driven and New Technologies for Students Living with Disabilities in STEM Education. ASEE CoNECD (Collaborative Network for Engineering and Computing Diversity) 2025.

[Presented at CoNECD conference on Feb 10, 2025 in San Antonio, Texas | Slides]