Rahul K. Dass

Jennifer M. Reddig, Glen R. Smith, Sanaz Ahmadzadeh Siyahrood et al.

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.

Rochan H. Madhusudhana, Rahul K. Dass, Jeanette Luu et al.

In online learning, the ability to provide quick and accurate feedback to learners is crucial. In skill-based learning, learners need to understand the underlying concepts and mechanisms of a skill to be able to apply it effectively. While videos are a common tool in online learning, they cannot comprehend or assess the skills being taught. Additionally, while Generative AI methods are effective in searching and retrieving answers from a text corpus, it remains unclear whether these methods exhibit any true understanding. This limits their ability to provide explanations of skills or help with problem-solving. This paper proposes a novel approach that merges Cognitive AI and Generative AI to address these challenges. We employ a structured knowledge representation, the TMK (Task-Method-Knowledge) model, to encode skills taught in an online Knowledge-based AI course. Leveraging techniques such as Large Language Models, Chain-of-Thought, and Iterative Refinement, we outline a framework for generating reasoned explanations in response to learners' questions about skills.

Xiao Jin, Rahul K. Dass, Ashok K. Goel

Intelligent tutoring systems excel at generating explanations but rarely provide principled diagnosis of where and why a learner is wrong. We introduce a misstep-aware coaching capability for Ivy, a neurosymbolic AI coach, built on a two-model architecture that augments a Task-Method-Knowledge (TMK) model with a new Pedagogical Model (PM) in the context of an online graduate AI course at Georgia Tech. The PM makes instructor diagnostic knowledge explicit and machine-readable by encoding, for each quiz question and incorrect response, the learner's underlying belief(a brief statement of the incorrect idea or missing knowledge), a TMK locus(the source of the misunderstanding), a misconception type and targeted scaffolding derived from the instructor's Q\&A key. Using quiz questions from the course, we demonstrate a proof-of-concept pipeline that detects and classifies learner errors and generates diagnosis-grounded scaffolding, moving Ivy beyond knowledge retrieval toward diagnostic misstep awareness, and enabling more precise, actionable feedback that supports conceptual change and advances adaptive learning systems in AI in education and the learning sciences.

Rahul K. Dass, Shubham Puri, Arpit Khandelwal et al.

Scalable AI tutoring for procedural skill learning requires structured knowledge representations, yet constructing these representations remains a labor-intensive bottleneck. This paper presents a human-in-the-loop text-to-model pipeline that uses large language models to transform instructional materials into schema-complete Task-Method-Knowledge models of procedural skills through ontology-constrained prompting and template-based generation. The approach automates structural scaffolding while preserving expert oversight for validating causal transitions and failure conditions. We apply the pipeline to instructional materials from a graduate-level online AI course, constructing 23 procedural skill models. AI-assisted authoring reduced expert modeling time by 50-70% while producing structurally valid and highly reproducible models under fixed-input conditions. We evaluate structural validity, semantic alignment, reproducibility, and refinement effort to characterize authoring scalability. Results indicate that AI-assisted text-to-model methods can substantially lower the cost of constructing structured procedural representations, making course-wide deployment of structured AI coaching systems practically feasible.

Sarah Elshabrawy, Rahul K. Dass, Ashok K. Goel

Evaluating procedural reasoning in AI-supported learning systems requires question-answer datasets that are both learner-like and grounded in the instructional knowledge the system is expected to use. We study how TMK-based question generation strategies affect dataset quality for procedural and multi-hop reasoning. We compare three strategies: strict generation from Task-Method-Knowledge (TMK) models, transcript-first generation with post-hoc TMK filtering, and TMK-aware generation that combines transcripts with structured guidance. To evaluate generated items, we introduce a grounding validation framework based on closed-set evidence units extracted from TMK models. The framework measures whether answers are supported by the underlying representation, whether questions are self-contained, and whether they target multi-hop procedural reasoning. Across 23 instructional topics and 690 generated question-answer pairs, strict TMK generation achieves the strongest overall quality, with 96.5% grounded questions and 92.6% usable questions. Transcript-first generation produces more learner-like questions but more context-dependent or weakly grounded items, while TMK-aware generation yields high raw multi-hop coverage but lower grounding. These results show that procedural richness and natural phrasing do not guarantee representational grounding, motivating explicit representation-aware validation for evaluation datasets in AI-supported learning.