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arxiv: 2605.04616 · v1 · submitted 2026-05-06 · 💻 cs.CY · cs.AI

Recognition: unknown

Guidelines for Designing AI Technologies to Support Adult Learning

Alex Endert, Ashok Goel, Chris Dede, Christopher J. MacLellan, Daniel Weitekamp, Glen R. Smith Jr, Jennifer M. Reddig, Jinho Kim, John Kos, Kaitlyn Crutcher, Min Kyu Kim, Momin Naushad Siddiqui, Ploy Thajchayapong, Rahul K. Dass, Sanaz Ahmadzadeh Siyahrood, Sandeep Kakar, Scott Crossley, Wesley G. Morris, Yoojin Bae

Pith reviewed 2026-05-08 16:45 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords AI in educationadult learningdesign guidelinesthematic analysiseducational technologyheuristic evaluationonline learning
0
0 comments X

The pith

Analysis of real AI deployments produces 19 guidelines for adult learning technologies

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

AI-powered educational technologies have shown benefits but are mostly designed around K-12 learners, leaving adult education systems misaligned with adults' schedules, motivations, and life demands. This paper studies several AI learning technologies deployed over time at a national institute dedicated to adult and online education. The authors apply reflexive thematic analysis to the data and extract recurring challenges and design considerations. They combine these into nineteen guidelines and check their usefulness by evaluating the original systems against them. A tool is also offered to help connect the guidelines to specific stakeholder comments from the analysis.

Core claim

The paper establishes that reflexive thematic analysis of longitudinal deployment data from AI-supported adult learning systems can identify recurring challenges, which are then synthesized into a set of 19 design guidelines. These guidelines are demonstrated to be useful through heuristic evaluation of the systems, and a guideline exploration tool is presented to link the guidelines to stakeholder statements.

What carries the argument

Reflexive thematic analysis of deployment data to derive design guidelines for AI in adult learning

If this is right

  • The 19 guidelines should inform the design of future AI-supported adult learning technologies.
  • Heuristic evaluation provides a way to assess existing systems against the guidelines.
  • The guideline exploration tool can assist in generating new technology ideas based on stakeholder needs.
  • Adult learners benefit when AI tools account for their distinct constraints compared to younger students.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The guidelines may require testing and refinement when used in adult learning contexts different from the national institute studied.
  • Following these guidelines could improve learner engagement and completion rates in adult education programs.
  • Similar analysis methods could generate domain-specific guidelines for AI in other educational or training settings.

Load-bearing premise

The patterns of challenges found in the authors' own AI systems are general enough to guide design in other adult learning settings and technologies.

What would settle it

Deploying AI learning tools in other adult education programs that follow the 19 guidelines but still encounter significant unaddressed problems would indicate the guidelines are not broadly applicable.

Figures

Figures reproduced from arXiv: 2605.04616 by Alex Endert, Ashok Goel, Chris Dede, Christopher J. MacLellan, Daniel Weitekamp, Glen R. Smith Jr, Jennifer M. Reddig, Jinho Kim, John Kos, Kaitlyn Crutcher, Min Kyu Kim, Momin Naushad Siddiqui, Ploy Thajchayapong, Rahul K. Dass, Sanaz Ahmadzadeh Siyahrood, Sandeep Kakar, Scott Crossley, Wesley G. Morris, Yoojin Bae.

Figure 1
Figure 1. Figure 1: Connections between institution types (research universities and technical colleges), AI-ALOE technologies, and view at source ↗
Figure 2
Figure 2. Figure 2: Affinity diagramming process used to derive design guidelines from qualitative data. Stakeholder quotes (yellow view at source ↗
Figure 3
Figure 3. Figure 3: Design guidelines organized across three overlapping Community of Inquiry dimensions: Cognitive (learner experience view at source ↗
Figure 4
Figure 4. Figure 4: Number of stakeholder comments by design guideline, broken down by group (research team, students, and instructors). view at source ↗
Figure 5
Figure 5. Figure 5: Average satisfaction ratings for each design guideline across the research institute’s technologies. Green bubbles show view at source ↗
Figure 6
Figure 6. Figure 6: The Guideline Explorer interface for browsing and filtering ideation statements associated with each design guideline. view at source ↗
Figure 7
Figure 7. Figure 7: Example interfaces from SMART illustrating how learner data can be transformed into actionable insights for different view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The paper reports on a reflexive thematic analysis of longitudinal deployment data from multiple AI-powered learning technologies developed at a US national research institute focused on adult and online education. Recurring challenges and design considerations are identified and synthesized into 19 guidelines for future AI-supported adult learning systems. Utility is demonstrated by applying these guidelines in a heuristic evaluation of the original deployed systems, and a guideline exploration tool is presented that links the guidelines to stakeholder statements from the analysis.

Significance. If the guidelines hold beyond the source context, the work would address a clear gap in AIED research, which has focused predominantly on K-12 settings, by offering adult-learner-specific design considerations grounded in actual deployment experience. The provision of a practical exploration tool that connects guidelines to real stakeholder statements is a concrete strength that could aid practitioners in ideation. The contribution is timely given increasing interest in lifelong and online learning technologies.

major comments (1)
  1. [Heuristic evaluation] Heuristic evaluation section: The utility of the 19 guidelines is demonstrated solely by re-applying them heuristically to the identical deployed systems that supplied the longitudinal data for the reflexive thematic analysis. This tests consistency with the authors' prior observations rather than independent generalizability, applicability to other adult-learning contexts, or value for novel AI designs. No external systems, prospective design exercises, or baseline comparisons are described, weakening support for the claim that the guidelines can inform future technologies.
minor comments (3)
  1. [Methods] Methods section: The reflexive thematic analysis description provides insufficient detail on the number of systems and data volume analyzed, the coding process (including number of analysts and any reliability checks), and how saturation was determined. These omissions make it difficult to assess the robustness and representativeness of the resulting 19 guidelines.
  2. [Results] Results and synthesis: The mapping from identified themes to the specific set of 19 guidelines is not transparent. A table or diagram showing how themes were consolidated into guidelines would improve clarity and allow readers to trace the derivation.
  3. [Guideline exploration tool] Guideline exploration tool: Additional information is needed on the tool's technical implementation, user interface, and any evaluation of its effectiveness in supporting ideation beyond the authors' own use.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address the major comment below and have revised the manuscript to better frame the scope and limitations of our demonstration.

read point-by-point responses

  1. Referee: Heuristic evaluation section: The utility of the 19 guidelines is demonstrated solely by re-applying them heuristically to the identical deployed systems that supplied the longitudinal data for the reflexive thematic analysis. This tests consistency with the authors' prior observations rather than independent generalizability, applicability to other adult-learning contexts, or value for novel AI designs. No external systems, prospective design exercises, or baseline comparisons are described, weakening support for the claim that the guidelines can inform future technologies.

    Authors: We acknowledge that the heuristic evaluation is performed on the same systems used to derive the guidelines via reflexive thematic analysis. This serves as an internal demonstration of how the guidelines can surface and organize design considerations from the original deployment data, rather than an independent test of generalizability. We agree this limits claims about applicability to entirely new contexts or novel designs. To address the concern, we have revised the manuscript as follows: (1) clarified in the heuristic evaluation section that the exercise illustrates practical utility and consistency within the source context; (2) added a dedicated Limitations subsection explicitly noting the absence of external validation and calling for future studies with other adult-learning systems and prospective design exercises; and (3) expanded the Discussion with concrete examples of how the guidelines, linked to stakeholder statements, could guide ideation for new AI technologies outside the original deployments. These changes better scope the contribution without overstating the validation provided. revision: yes

Circularity Check

1 steps flagged

Heuristic evaluation of the 19 guidelines performed only on the same deployed systems used to derive them via reflexive thematic analysis

specific steps
  1. other [Abstract]
    "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."

    The guidelines are synthesized directly from analysis of the authors' own deployed systems; the utility demonstration then re-applies the guidelines heuristically to those exact same systems. This reduces the 'demonstration' step to a consistency check on the input data rather than an independent test of usefulness for future technologies.

full rationale

The paper derives the guidelines from reflexive thematic analysis of longitudinal data on the authors' own deployed AI learning systems, then claims to demonstrate their utility by applying a heuristic evaluation to those identical systems. This creates a self-referential loop where the 'demonstration' largely confirms consistency with the original observations rather than providing independent evidence of generalizability or value for novel designs. No external systems, prospective applications, or baseline comparisons are described. However, the core synthesis of guidelines from qualitative data remains a valid contribution independent of the evaluation step, so the circularity is partial and does not render the entire paper equivalent to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of standard qualitative methods in education technology and HCI; no numerical free parameters or newly invented entities are introduced.

axioms (1)
  • domain assumption Reflexive thematic analysis applied to longitudinal deployment data can reliably surface generalizable design challenges for AI adult learning systems.
    Invoked to justify synthesizing the 19 guidelines from the authors' internal systems.

pith-pipeline@v0.9.0 · 5539 in / 1178 out tokens · 53742 ms · 2026-05-08T16:45:45.031298+00:00 · methodology

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