Intelligent Tutors for Adult Learners: An Analysis of Needs and Challenges

TL;DR

This work investigates the adoption of intelligent tutoring systems by adult, non traditional learners and develops Apprentice Tutors to address their unique needs. Through a deployment study and focus group analysis, it links sociotechnical factors such as curriculum alignment, onboarding, and instructor engagement to adoption and effectiveness. The authors present a rule based expert system with Bayesian knowledge tracing, real time feedback, multi layer hints, and visual dashboards, and derive design recommendations to improve adult learning outcomes. The findings offer actionable guidance for designing lifelong learning tools that fit busy adult learners and integrate smoothly with existing instructional ecosystems.

Abstract

This work examines the sociotechnical factors that influence the adoption and usage of intelligent tutoring systems in self-directed learning contexts, focusing specifically on adult learners. The study is divided into two parts. First, we present Apprentice Tutors, a novel intelligent tutoring system designed to address the unique needs of adult learners. The platform includes adaptive problem selection, real-time feedback, and visual dashboards to support learning in college algebra topics. Second, we investigate the specific needs and experiences of adult users through a deployment study and a series of focus groups. Using thematic analysis, we identify key challenges and opportunities to improve tutor design and adoption. Based on these findings, we offer actionable design recommendations to help developers create intelligent tutoring systems that better align with the motivations and learning preferences of adult learners. This work contributes to a wider understanding of how to improve educational technologies to support lifelong learning and professional development.

Figures (2)

• Figure 1: User interface of Apprentice Tutors platform with key features: (a) penalization through adaptive problem selection (b) real-time correctness feedback (c) four available tutors (d) hint box and multi-layer hints (e) user profile screen with progress bars corresponding to KCs.
• Figure 2: Graphical representation of student engagement: This figure shows the flow of student interaction with the tutoring program, starting with total access and dividing into students who used or did not use tutors. It tracks progress through problem completion levels, highlighting those who completed no problems, one problem, and multiple problems, with further breakdowns for students stopping at specific milestones (e.g., 2, 3, 4, or 5 problems).