Guidelines for Designing AI Technologies to Support Adult Learning

📅 2026-05-06
📈 Citations: 0
Influential: 0
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📝 Abstract
AI-powered educational technologies have demonstrated measurable benefits for learners, but their design and evaluation have largely centered on K-12 contexts. As a result, many AI-supported learning systems remain poorly aligned with the needs, constraints, and goals of adult learners. To better understand how AI systems function in adult education, this paper examines the deployment of several AI learning technologies developed within a multidisciplinary, national research institute in the United States focused on adult learning and online education. Drawing on longitudinal deployment data, we conducted a reflexive thematic analysis to identify recurring challenges and design considerations across systems. These insights were synthesized into a set of 19 design guidelines intended to inform future AI-supported adult learning technologies. We demonstrate the utility of these guidelines through a heuristic evaluation of the deployed systems. Lastly, we present a guideline exploration tool that aids in the ideation of technologies by connecting the guidelines to stakeholder statements surfaced in the analysis process.
Problem

Research questions and friction points this paper is trying to address.

adult learning
AI-powered educational technologies
design guidelines
learner needs
educational alignment
Innovation

Methods, ideas, or system contributions that make the work stand out.

AI in adult education
design guidelines
reflexive thematic analysis
heuristic evaluation
stakeholder-centered design
J
Jennifer M. Reddig
Georgia Institute of Technology
G
Glen R. Smith Jr
Georgia Institute of Technology
S
Sanaz Ahmadzadeh Siyahrood
Georgia Institute of Technology
W
Wesley G. Morris
Vanderbilt University
Y
Yoojin Bae
Georgia State University
K
Kaitlyn Crutcher
Georgia Institute of Technology
J
John Kos
Georgia Institute of Technology
Rahul K. Dass
Rahul K. Dass
Postdoc at Georgia Tech
AI for EducationNLPResponsible AIMLOpsComputer Vision
J
Jinho Kim
Georgia State University
M
Momin Naushad Siddiqui
Georgia Institute of Technology
Daniel Weitekamp
Daniel Weitekamp
Georgia Institute of Technology
Machine LearningEducational Technology
P
Ploy Thajchayapong
Georgia Institute of Technology
S
Sandeep Kakar
Georgia Institute of Technology
Alex Endert
Alex Endert
Associate Professor, Georgia Tech
human-computer interactioninformation visualizationvisual analytics
S
Scott Crossley
Vanderbilt University
Min Kyu Kim
Min Kyu Kim
Associate Professor of Learning Sciences at Georgia State University
Knowledge representationLearner engagementAI-Augmented Learning Environment
Chris Dede
Chris Dede
Wirth Professor in Learning Technologies
learning technologiesleadershippolicy
A
Ashok Goel
Georgia Institute of Technology
Christopher J. MacLellan
Christopher J. MacLellan
Assistant Professor, Georgia Institute of Technology
Cognitive SystemsArtificial Intelligence in EducationHuman-AI TeamingConcept Formation