Pith. sign in

REVIEW 4 major objections 6 minor 185 references

AI chatbots produce need-specific psychological benefits for engineering students, ranked from competence relief down to relatedness, and who benefits depends on baseline motivation and attention more than demographics.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-01 00:06 UTC pith:3SYE7QL3

load-bearing objection The paper's central three-way ranking of AI chatbot benefits is not statistically tested; the competence-autonomy gap is trivial, though relatedness does lag. the 4 major comments →

arxiv 2607.26338 v1 pith:3SYE7QL3 submitted 2026-07-28 cs.HC

Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes

Kevin Zhongyang Shao , Denise Wilson , Yale Quan , Sep Makhsous This is my paper
classification cs.HC
keywords AI chatbotsengineering educationpsychological needscompetence frustrationautonomyrelatednessinattentionstructural equation modeling
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper claims that AI chatbot use does not support all psychological needs equally. In a survey of 206 engineering undergraduates, students reported the strongest perceived benefit as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. The authors argue that these outcomes were driven less by demographic composition and more by students' starting motivational profile—baseline competence frustration, autonomy, a combined self-efficacy/self-regulated-learning factor they call personal agency, and self-reported inattention. They further claim that inattention moderates how baseline competence frustration and autonomy translate into perceived benefits, which would matter for designing adaptive, attention-aware AI learning tools.

Core claim

Using exploratory and confirmatory factor analysis followed by structural equation modeling with latent interactions, the study finds that higher baseline competence frustration and higher baseline autonomy predict greater perceived relief from competence frustration after AI chatbot use, while higher personal agency predicts less such relief and higher inattention predicts more. Higher baseline autonomy predicts perceived autonomy gains, whereas higher baseline relatedness predicts smaller perceived autonomy and relatedness gains. Two interaction effects show that as inattention increases, the positive links from baseline competence frustration to perceived competence relief, and from basel

What carries the argument

The load-bearing machinery is the structural equation model with latent interaction effects, built from survey items adapted from existing psychological scales. Baseline autonomy, competence frustration, relatedness, and a combined personal-agency factor (self-efficacy plus self-regulated learning, which loaded together in the sample) serve as latent predictors of three perceived AI-benefit constructs, with self-reported inattention and demographic effect-coded covariates also in the model. The latent interactions test whether inattention changes the strength of the baseline-to-outcome paths; this is what allows the authors to claim attention moderates benefit rather than simply raising or l

Load-bearing premise

The self-report items, after removing some reverse-worded and weak items, validly measure the intended psychological constructs, and the cross-sectional associations reflect baseline states shaping perceived AI benefit rather than common-method bias or reverse causation.

What would settle it

A pre/post or randomized study that measures actual competence gains (e.g., problem-solving accuracy) and finds no greater gain or perceived relief for competence than for autonomy or relatedness would undercut the ranking; likewise, a study with high statistical power that shows no attenuating interaction between inattention and baseline competence frustration on perceived relief would falsify the moderation claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • Design should shift from treating AI support as uniform to scaffolding competence first, with contingent help that scales down as personal agency rises.
  • Interfaces for attention-challenged learners should externalize task state (visible next steps, saved state, bounded subtasks) rather than simply offering more explanation.
  • Relatedness support should route learners toward human and peer contact instead of relying on conversational polish.
  • Evaluations of AI learning tools should measure need-specific mechanisms and test heterogeneous effects, not only average outcomes.
  • Feasible next steps include longitudinal or pre/post designs linking perceived relief to objective learning outcomes.

Where Pith is reading between the lines

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

  • If the competence-first ranking holds in other samples, a testable extension is whether AI chatbots are best deployed as short-term feedback instruments while autonomy and relatedness support is reserved for course design and human interaction.
  • The inattention moderation suggests a concrete adaptive rule: the same baseline competence frustration should trigger stronger scaffolding for students with low inattention than for those with high inattention, since high inattention weakens the translation.
  • The personal-agency diminishing-returns pattern predicts an expertise-reversal effect in AI tutoring that could be experimentally tested by varying scaffolding intensity across learners with high versus low agency.
  • Because the outcomes are perceived rather than observed, a longitudinal study could check whether the competence relief is durable or merely reflects the immediacy of chatbot feedback.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper reports a cross-sectional survey of 206 engineering students who used AI chatbots. The authors use EFA/CFA and SEM with latent interactions to examine how baseline autonomy, competence frustration, relatedness, personal agency, and inattention relate to perceived AI-related benefits for autonomy, competence frustration relief, and relatedness. The central claim is that perceived benefits are need-specific: strongest for relief from competence frustration, moderate for autonomy, and weakest for relatedness. They also claim that baseline motivational states matter more than demographics, and that inattention moderates two of the associations. The final SEM shows good fit (CFI .963, RMSEA .034, SRMR .059) and explains substantial variance in perceived competence relief (52.3%) and autonomy (37.5%), but only 9.7% in perceived relatedness.

Significance. If the rank ordering and moderation results held, the paper would offer a useful empirical contribution to SDT-based design of AI learning tools, with concrete implications for adaptive scaffolding and attention-aware interfaces. The study is transparent about its cross-sectional, self-report nature and uses appropriate psychometric machinery (EFA, CFI/RMSEA/SRMR, omega, latent interactions). The transparent reporting of item deletions and model iterations is a strength. However, the headline rank ordering is asserted without an inferential test, so the central contribution is not currently established. The moderation findings are more defensible but also depend on post-hoc measurement decisions and multiple testing.

major comments (4)
  1. [§4.3, Table 10; Abstract; §6 Conclusion] The paper's central claim—that perceived AI benefits are strongest for competence relief, moderate for autonomy, and weakest for relatedness—is never actually tested. Table 10 reports means of 3.02 (competence relief), 2.96 (autonomy), and 2.52 (relatedness) with SDs around 0.76–0.87 and n=206. The competence–autonomy difference of 0.06 scale points is well within sampling error. No paired comparison, repeated-measures test, or latent mean difference test is reported. The SEM R² differences and path significance do not establish mean differences among the three perceived outcomes. The abstract and conclusion assert the 'strongest/moderate/weakest' ordering, and §5.4's design implications are organized around it. The authors should add a formal inferential test (e.g., paired t-tests with appropriate adjustment, or a latent mean difference test within the SEM) and revise the claims accordi
  2. [§3.3.1, §3.3.3, §4.4] The measurement model is decided through a sequence of post-hoc item deletions whose cumulative effect on the conclusions is not quantified. Reverse-worded items (n=3) are removed before EFA; ACO2 is removed from perceived competence; CO2 is removed after SEM inspection due to low loading; SR4 is removed for borderline loading. The paper acknowledges these decisions in §5.5, but there is no sensitivity analysis showing whether the rank ordering or the interaction effects are robust to including/excluding these items. For a claim that rests on comparing three outcome constructs, the authors should at least report the models with the deleted items included (or a conservative robustness check), and should specify whether the pattern of means and structural paths is stable. Without this, the possibility that item deletion artifacts drive the specificity of the results remains live.
  3. [§3.2, §5.5] The perceived-outcome items and baseline items come from the same self-report instrument, and the wording of the perceived items is semantically very close to the baseline items (e.g., 'I feel confident...' vs. 'has caused me to feel more confident...'). This raises common-method variance and carry-over concerns that are acknowledged only partially. The interpretation in §5.2 and §5.3 that baseline states 'translated into' perceived benefits is not supported by the cross-sectional design; it could equally reflect response style or reverse causation (e.g., students who perceive AI as helpful then report their baseline as more frustrated). The authors should either add a common-method-variance test (e.g., a marker variable or CFA-based method factor) or substantially soften the causal language throughout the discussion, restricting claims to concurrent associations.
  4. [§4.4.1] The latent interaction effects are reported with p-values but without simple slopes, regions of significance, or any correction for the number of interactions tested. Given that many interactions were examined (competence frustration × inattention, autonomy × inattention, personal agency × inattention, relatedness × inattention, presumably for each outcome), the two significant effects could be chance findings. The authors should report the number of tests, provide a plot or simple-slope analysis at representative levels of inattention, and either control the false-discovery rate or explicitly frame the interactions as exploratory. The current presentation gives these two interactions a prominence in the abstract and conclusion that the evidence does not yet support.
minor comments (6)
  1. [§3.1] The exclusion rate is high: 94 of 335 submissions failed an IMC. It would be helpful to report whether the 206 chatbot-users differ systematically from the excluded participants on any available demographics, since attrition could affect generalizability.
  2. [Table 2] The effect coding table is clear but the 'Other gender' category includes 'Prefer not to answer', 'Non-binary', and 'Other', which are distinct response options. Aggregating them may obscure meaningful variation; this should be justified or at least restated as a limitation.
  3. [§5.2] The phrase 'personal agency mattered more than demographic composition' is not directly supported by a formal comparison of effect sizes or variance explained. Consider reporting a model comparison or standardized effect-size table to substantiate this claim.
  4. [§3.3.3] The modsem package product-indicator method is suitable for normally distributed indicators, but the observed variables are 5-point Likert items. The authors should discuss or test the robustness of the latent interaction results to treating items as ordinal rather than continuous.
  5. [Table 10] Inattention is scored 0–36 but the other scales are 1–5; the table caption could clarify that this is a sum score, not a mean. Also, the row for competence frustration says '1' footnote but the table shows one row; the footnote numbering appears inconsistent with the table body.
  6. [Abstract] The word 'impact' in RQ1 and the abstract overstates what a cross-sectional survey can establish. Consider replacing with 'association with' or 'perceived association with' throughout the research questions.

Circularity Check

0 steps flagged

Empirical survey paper; no derivation chain to reduce; minor same-instrument overlap noted as validity risk, not circularity.

full rationale

This paper makes empirical claims from a cross-sectional survey; there is no formal derivation chain whose outputs could be equivalent to its inputs by construction. The central ranking (competence relief 3.02 vs autonomy 2.96 vs relatedness 2.52) is a descriptive comparison, not a derived quantity; it is under-analyzed statistically, but lack of an inferential test is not circularity. The SEM associations are estimated from data, not imposed. The inattention moderation results are empirical interactions. The one potential overlap is that perceived AI outcome items reuse the baseline item stems with change prefixes ('has improved my sense of choice and freedom...' Table 14 vs 'I feel a sense of choice and freedom...' Table 13), and the strong path from baseline autonomy to perceived autonomy (β=.58) may be inflated by common-method and semantic overlap; this is a measurement validity threat the paper itself partially acknowledges in §5.5, not a definitional equivalence. The citation to the authors' own Engineering CAReS instrument [141] is one of several scale sources and is not load-bearing for the conclusions. Overall: no significant circularity; score 1.

Axiom & Free-Parameter Ledger

3 free parameters · 6 axioms · 0 invented entities

'Personal agency' is a data-derived label for a combined self-efficacy/SRL factor, not a new entity. The central claim rests on SDT's taxonomy, the validity of adapted self-report scales, and the assumption that associations are not badly distorted by common-method variance or unmeasured confounders.

free parameters (3)
  • EFA loading threshold = 0.30
    Items retained only if EFA loadings exceed .30; conventional but arbitrary, and influences which items define each construct.
  • Post hoc item deletions = SR4, ACO2, CO2 removed; 3 reverse-worded items removed
    Scale composition was adjusted after inspecting the same sample's EFA/SEM loadings; these deletions changed construct coverage and fit statistics.
  • Latent factor structure = 4 baseline factors, 3 perceived-AI factors, 1 inattention factor
    Factor counts derived from sample-specific parallel analysis; merging self-regulated learning and self-efficacy into 'personal agency' is not a pre-specified theoretical construct.
axioms (6)
  • domain assumption Self-Determination Theory's three basic needs (autonomy, competence, relatedness) are the correct organizing framework for AI learning outcomes.
    The study's constructs, measures, and interpretation all presuppose SDT's need taxonomy, introduced in §1 and §2.1; if the taxonomy is wrong for AI-mediated learning, the outcomes are mis-specified.
  • ad hoc to paper Adapted items measure the intended latent constructs after item deletion.
    Scales were adapted from validated instruments; reverse-worded items, SR4, ACO2, and CO2 were removed after inspecting the same sample, with no external revalidation (§3.3.1, §4.1, §4.4).
  • domain assumption Self-reported perceived outcomes are sufficiently accurate proxies for psychological need support.
    All dependent variables are perceptions rather than objective measures; this is acknowledged in §5.5.
  • domain assumption ASRS inattention items measure attentional difficulty in this sample.
    The attention measure is self-report, not clinical diagnosis; the authors note this limitation in §5.5.
  • domain assumption The cross-sectional SEM associations are not materially biased by unmeasured confounders or reverse causation.
    Observational design; authors interpret associations as insights for design and acknowledge causality limits, but the ranking and moderation claims still require this assumption to hold.
  • standard math Standard SEM identification and estimation assumptions hold.
    Robust maximum likelihood, model identification, and fit indices are used; no formal machine-checked verification.

pith-pipeline@v1.3.0-alltime-deepseek · 27862 in / 12121 out tokens · 119421 ms · 2026-08-01T00:06:13.194123+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes." pith.science (2026). https://pith.science/paper/3SYE7QL3

@misc{pith2026260726338,
  author       = {Pith},
  title        = {Pith review of: Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3SYE7QL3}},
  note         = {Machine review of arXiv:2607.26338}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Artificial Intelligence (AI) is transforming higher education, but its benefits can vary depending on where, how, and how often it supports learning. While prior research emphasizes cognitive and academic outcomes, this study examines how AI chatbots support the psychological needs and motivational states of engineering students. A survey of college engineering students (n = 206) examined perceived effects of AI chatbots on autonomy, relatedness, and relief from competence frustration. Structural equation modeling with latent interaction effects examined how baseline autonomy, competence frustration, relatedness, and personal agency contributed to perceived AI outcomes. Results indicate that students perceived that AI provided the greatest benefits as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. Baseline motivational states mattered more than demographic factors, and inattention moderated how baseline competence frustration and autonomy related to perceived AI-related benefits. These results offer insights into formulating design principles for engineering-specific AI-based tools.

Figures

Figures reproduced from arXiv: 2607.26338 by Denise Wilson, Kevin Zhongyang Shao, Sep Makhsous, Yale Quan.

Figure 1
Figure 1. Figure 1: Overview of the analysis procedure. After response screening, exploratory factor analysis (EFA) was [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Structural equation model of baseline psychological constructs and perceived need fulfillment with [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

185 extracted references · 3 linked inside Pith

  1. [1]

    Herman Aguinis, Jeffrey R Edwards, and Kyle J Bradley. 2017. Improving our understanding of moderation and mediation in strategic management research.Organizational research methods20, 4 (2017), 665–685

    2017

  2. [2]

    Vincent Aleven, Ido Roll, Bruce M McLaren, and Kenneth R Koedinger. 2016. Help helps, but only so much: Research on help seeking with intelligent tutoring systems.International Journal of Artificial Intelligence in Education26, 1 (2016), 205–223

    2016

  3. [3]

    María Álvarez-Godos, Camino Ferreira, and María-José Vieira. 2023. A systematic review of actions aimed at university students with ADHD.Frontiers in psychology14 (2023), 1216692. Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes 23

    2023

  4. [4]

    2024.Profiles of Engineering and Engineering Technology, 2023

    American Society for Engineering Education. 2024.Profiles of Engineering and Engineering Technology, 2023. Technical Report. American Society for Engineering Education, Washington, DC. https://ira.asee.org/by-the-numbers/

    2024

  5. [5]

    Dawn Anderson-Butcher and David E Conroy. 2002. Factorial and criterion validity of scores of a measure of belonging in youth development programs.Educational and psychological measurement62, 5 (2002), 857–876

    2002

  6. [6]

    Prathibha Sathyanjalee Ariyasena, Nimthara Nuwangi Bandara Munasinghe, Christan Fryer Cooraydas Chandra, Prashanthi Anushika Dissanayake, Uthpala Samarakoon, and Wishalya Tissera. 2024. Exploring the Utility of Gamified Learning to Support Academic Progress among ADHD-Diagnosed Children. InProceedings of the 2024 Sixteenth International Conference on Cont...

    2024

  7. [7]

    I’d Never Actually Realized How Big An Impact It Had Until Now

    Alex Atcheson, Omar Khan, Brian Siemann, Anika Jain, and Karrie Karahalios. 2025. " I’d Never Actually Realized How Big An Impact It Had Until Now": Perspectives of University Students with Disabilities on Generative Artificial Intelligence. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–22

    2025

  8. [8]

    Robert K Atkinson, Alexander Renkl, and Mary Margaret Merrill. 2003. Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps.Journal of educational psychology 95, 4 (2003), 774

    2003

  9. [9]

    Roger Azevedo, François Bouchet, Melissa Duffy, Jason Harley, Michelle Taub, Gregory Trevors, Elizabeth Cloude, Daryn Dever, Megan Wiedbusch, Franz Wortha, et al. 2022. Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system.Frontiers in Psychology13 (2022), 813632

    2022

  10. [10]

    Xuemei Bai and Xiaoqing Gu. 2022. Effect of teacher autonomy support on the online self-regulated learning of students during COVID-19 in China: The chain mediating effect of parental autonomy support and students’ self-efficacy.Journal of Computer Assisted Learning38, 4 (2022), 1173–1184

    2022

  11. [11]

    Nick Ballou, Sebastian Deterding, April Tyack, Elisa D Mekler, Rafael A Calvo, Dorian Peters, Gabriela Villalobos- Zúñiga, and Selen Turkay. 2022. Self-determination theory in HCI: shaping a research agenda. InCHI conference on human factors in computing systems extended abstracts. 1–6

    2022

  12. [12]

    1997.Self-efficacy: The exercise of control

    Albert Bandura. 1997.Self-efficacy: The exercise of control. Vol. 11. Freeman

    1997

  13. [13]

    Russell A Barkley. 1997. Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD.Psychological bulletin121, 1 (1997), 65

    1997

  14. [14]

    Maurice S Bartlett. 1950. Tests of significance in factor analysis.British journal of psychology(1950)

    1950

  15. [15]

    Lindsey D Basileo, Barbara Otto, Merewyn Lyons, Natalie Vannini, and Michael D Toth. 2024. The role of self-efficacy, motivation, and perceived support of students’ basic psychological needs in academic achievement. InFrontiers in education, Vol. 9. Frontiers Media SA, 1385442

    2024

  16. [16]

    Beckwith, M

    L. Beckwith, M. Burnett, S. Wiedenbeck, C. Cook, S. Sorte, and M. Hastings. 2005. Effectiveness of End-User Debugging Software Features: Are There Gender Issues?. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’05). ACM, New York, NY, USA, 869–878

    2005

  17. [17]

    Beckwith, C

    L. Beckwith, C. Kissinger, M. Burnett, S. Wiedenbeck, J. Lawrance, A. Blackwell, and C. Cook. 2006. Tinkering and Gender in End-User Programmers’ Debugging. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’06). ACM, New York, NY, USA, 231–240

    2006

  18. [18]

    Timothy W Bickmore and Rosalind W Picard. 2005. Establishing and maintaining long-term human-computer relationships.ACM Transactions on Computer-Human Interaction (TOCHI)12, 2 (2005), 293–327

    2005

  19. [19]

    Aaron E Black and Edward L Deci. 2000. The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective.Science education84, 6 (2000), 740–756

    2000

  20. [20]

    Jonas Blattgerste, Jannik Franssen, Michaela Arztmann, and Thies Pfeiffer. 2022. Motivational benefits and usability of a handheld Augmented Reality game for anatomy learning. In2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). IEEE, 266–274

    2022

  21. [21]

    Victoria Cabales. 2019. Muse: Scaffolding metacognitive reflection in design-based research. InExtended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1–6

    2019

  22. [22]

    John B Carroll. 1953. An analytical solution for approximating simple structure in factor analysis.Psychometrika18, 1 (1953), 23–38

    1953

  23. [23]

    José-Antonio Cervantes, Sonia López, Salvador Cervantes, Aribei Hernández, and Heiler Duarte. 2023. Social robots and brain–computer interface video games for dealing with attention deficit hyperactivity disorder: A systematic review.Brain Sciences13, 8 (2023), 1172

    2023

  24. [24]

    Beiwen Chen, Maarten Vansteenkiste, Wim Beyers, Liesbet Boone, Edward L Deci, Jolene Van der Kaap-Deeder, Bart Duriez, Willy Lens, Lennia Matos, Athanasios Mouratidis, et al. 2015. Basic psychological need satisfaction and frustration scale.Motivation and Emotion(2015)

    2015

  25. [25]

    Beiwen Chen, Maarten Vansteenkiste, Wim Beyers, Liesbet Boone, Edward L Deci, Jolene Van der Kaap-Deeder, Bart Duriez, Willy Lens, Lennia Matos, Athanasios Mouratidis, et al. 2015. Basic psychological need satisfaction, need 24 Shao et al. frustration, and need strength across four cultures.Motivation and emotion39, 2 (2015), 216–236

    2015

  26. [26]

    Jing Chen. 2022. The effectiveness of self-regulated learning (SRL) interventions on L2 learning achievement, strategy employment and self-efficacy: A meta-analytic study.Frontiers in Psychology13 (2022), 1021101

    2022

  27. [27]

    John Chen, Xi Lu, Yuzhou Du, Michael Rejtig, Ruth Bagley, Mike Horn, and Uri Wilensky. 2024. Learning agent-based modeling with llm companions: Experiences of novices and experts using chatgpt & netlogo chat. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–18

    2024

  28. [28]

    Thomas KF Chiu, Benjamin Luke Moorhouse, Ching Sing Chai, and Murod Ismailov. 2024. Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot.Interactive Learning Environments32, 7 (2024), 3240–3256

    2024

  29. [29]

    Anna B Costello and Jason Osborne. 2005. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis.Practical assessment, research, and evaluation10, 1 (2005)

    2005

  30. [30]

    Marcus Credé and L Alison Phillips. 2011. A meta-analytic review of the Motivated Strategies for Learning Question- naire.Learning and individual differences21, 4 (2011), 337–346

    2011

  31. [31]

    what" and

    Edward L Deci and Richard M Ryan. 2000. The" what" and" why" of goal pursuits: Human needs and the self- determination of behavior.Psychological inquiry11, 4 (2000), 227–268

    2000

  32. [32]

    2013.Intrinsic motivation and self-determination in human behavior

    Edward L Deci and Richard M Ryan. 2013.Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media

    2013

  33. [33]

    Charlotte Dignath, Gerhard Buettner, and Hans-Peter Langfeldt. 2008. How can primary school students learn self- regulated learning strategies most effectively?: A meta-analysis on self-regulation training programmes.Educational Research Review3, 2 (2008), 101–129

    2008

  34. [34]

    Hamzeh Dodeen. 2023. The effects of changing negatively worded items to positively worded items on the reliability and the factor structure of psychological scales.Journal of Psychoeducational Assessment41, 3 (2023), 298–310

    2023

  35. [35]

    Jinming Du and Ben Kei Daniel. 2024. Transforming language education: A systematic review of AI-powered chatbots for English as a foreign language speaking practice.Computers and Education: Artificial Intelligence6 (2024), 100230

    2024

  36. [36]

    Xiaojing Duan, Chaoli Wang, and Guieswende Rouamba. 2022. Designing a learning analytics dashboard to provide students with actionable feedback and evaluating its impacts. InProceedings of International Conference on Computer Supported Education

    2022

  37. [37]

    Daniel Elford, Simon J Lancaster, and Garth A Jones. 2022. Fostering motivation toward chemistry through augmented reality educational escape activities. A self-determination theory approach.Journal of chemical education99, 10 (2022), 3406–3417

    2022

  38. [38]

    Leandre R Fabrigar, Duane T Wegener, Robert C MacCallum, and Erin J Strahan. 1999. Evaluating the use of exploratory factor analysis in psychological research.Psychological methods4, 3 (1999), 272

    1999

  39. [39]

    Kate S Glazko, Momona Yamagami, Aashaka Desai, Kelly Avery Mack, Venkatesh Potluri, Xuhai Xu, and Jennifer Mankoff. 2023. An autoethnographic case study of generative artificial intelligence’s utility for accessibility. In Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility. 1–8

    2023

  40. [40]

    Saeideh Goharinejad, Samira Goharinejad, Sadrieh Hajesmaeel-Gohari, and Kambiz Bahaadinbeigy. 2022. The usefulness of virtual, augmented, and mixed reality technologies in the diagnosis and treatment of attention deficit hyperactivity disorder in children: an overview of relevant studies.BMC psychiatry22, 1 (2022), 4

    2022

  41. [41]

    Arthur C Graesser. 2016. Conversations with AutoTutor help students learn.International Journal of Artificial Intelligence in Education26, 1 (2016), 124–132

    2016

  42. [42]

    James M Graham. 2006. Congeneric and (essentially) tau-equivalent estimates of score reliability: What they are and how to use them.Educational and psychological measurement66, 6 (2006), 930–944

    2006

  43. [43]

    Kandice N Green, Shengjie Yao, Heejae Lee, Lyndsay Michalik Gratch, David Peters, and T Makana Chock. 2024. Understanding Expressions of Self-Determination Theory in the Evaluation of IDEA-Themed VR Storytelling.Media and Communication12 (2024)

    2024

  44. [44]

    Rashmi Gunawardana, Michelle Perera, Rashini Lakshika, Chaminda Ranasinghe, and Kasun Karunanayaka. 2025. Enhancing Adaptive Personalized Learning Interfaces with Generative AI for Individuals with ADHD. InProceedings of the 16th International Conference of Human-Computer Interaction (HCI) Design & Research. 131–143

    2025

  45. [45]

    Khe Foon Hew, Weijiao Huang, Jiahui Du, and Chengyuan Jia. 2023. Using chatbots to support student goal setting and social presence in fully online activities: Learner engagement and perceptions.Journal of Computing in Higher Education35, 1 (2023), 40–68

    2023

  46. [46]

    D Hooper, J Coughlan, MR Mullen, J Mullen, D Hooper, J Coughlan, and MR Mullen. 2008. Structural Equation Modelling: Guidelines for Determining Model Fit Structural equation modelling: guidelines for determining model fit. Dublin Institute of Technology ARROW@ DIT, 6 (1), 53–60

    2008

  47. [47]

    Li-tze Hu and Peter M Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.Structural equation modeling: a multidisciplinary journal6, 1 (1999), 1–55. Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes 25

    1999

  48. [48]

    Anna YQ Huang, Owen HT Lu, and Stephen JH Yang. 2023. Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom.Computers & Education194 (2023), 104684

    2023

  49. [49]

    Brockmole, and Sidney K

    Stephen Hutt, Kristina Krasich, James R. Brockmole, and Sidney K. D’Mello. 2021. Breaking out of the lab: Mitigating mind wandering with gaze-based attention-aware technology in classrooms. InProceedings of the 2021 CHI conference on human factors in computing systems. 1–14

    2021

  50. [50]

    Kiran Ijaz, Naseem Ahmadpour, Yifan Wang, and Rafael A Calvo. 2020. Player experience of needs satisfaction (PENS) in an immersive virtual reality exercise platform describes motivation and enjoyment.International Journal of Human–Computer Interaction36, 13 (2020), 1195–1204

    2020

  51. [51]

    Hyungshim Jang, Eun Joo Kim, and Johnmarshall Reeve. 2012. Longitudinal test of self-determination theory’s motivation mediation model in a naturally occurring classroom context.Journal of Educational psychology104, 4 (2012), 1175

    2012

  52. [52]

    Hyungshim Jang, Johnmarshall Reeve, and Edward L Deci. 2010. Engaging students in learning activities: It is not autonomy support or structure but autonomy support and structure.Journal of educational psychology102, 3 (2010), 588

    2010

  53. [53]

    Robert I Jennrich and PF Sampson. 1966. Rotation for simple loadings.Psychometrika31, 3 (1966), 313–323

    1966

  54. [54]

    Daeun Jeong, Sungbok Shin, and Jongwook Jeong. 2025. Conversation Progress Guide: UI system for enhancing self-efficacy in conversational AI. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–11

    2025

  55. [55]

    Hyoungwook Jin, Seonghee Lee, Hyungyu Shin, and Juho Kim. 2024. Teach ai how to code: Using large language models as teachable agents for programming education. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–28

    2024

  56. [56]

    Henry F Kaiser. 1958. The varimax criterion for analytic rotation in factor analysis.Psychometrika23, 3 (1958), 187–200

    1958

  57. [57]

    Henry F Kaiser. 1970. A second generation little jiffy.Psychometrika35, 4 (1970), 401–415

    1970

  58. [58]

    Slava Kalyuga. 2007. Expertise reversal effect and its implications for learner-tailored instruction.Educational psychology review19, 4 (2007), 509–539

    2007

  59. [59]

    Slava Kalyuga. 2009. The expertise reversal effect. InManaging cognitive load in adaptive multimedia learning. IGI Global Scientific Publishing, 58–80

    2009

  60. [60]

    Chester Chun Seng Kam. 2023. Why do regular and reversed items load on separate factors? Response difficulty vs. item extremity.Educational and Psychological Measurement83, 6 (2023), 1085–1112

    2023

  61. [61]

    Stuart A Karabenick and Myron H Dembo. 2011. Understanding and facilitating self-regulated help seeking.New directions for teaching and learning2011, 126 (2011), 33–43

    2011

  62. [62]

    Majeed Kazemitabaar, Runlong Ye, Xiaoning Wang, Austin Zachary Henley, Paul Denny, Michelle Craig, and Tovi Grossman. 2024. Codeaid: Evaluating a classroom deployment of an llm-based programming assistant that balances student and educator needs. InProceedings of the 2024 chi conference on human factors in computing systems. 1–20

    2024

  63. [63]

    David A Kenny, Burcu Kaniskan, and D Betsy McCoach. 2015. The performance of RMSEA in models with small degrees of freedom.Sociological methods & research44, 3 (2015), 486–507

    2015

  64. [64]

    Ronald C Kessler, Lenard Adler, Minnie Ames, Olga Demler, Steve Faraone, EVA Hiripi, Mary J Howes, Robert Jin, Kristina Secnik, Thomas Spencer, et al. 2005. The World Health Organization Adult ADHD Self-Report Scale (ASRS): a short screening scale for use in the general population.Psychological medicine35, 2 (2005), 245–256

    2005

  65. [65]

    Jeanine Kirchner-Krath, Manuel Schmidt-Kraepelin, Sofia Schöbel, Mathias Ullrich, Ali Sunyaev, and Harald FO Von Korflesch. 2024. Outplay your weaker self: A mixed-methods study on gamification to overcome procrastination in academia. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–19

    2024

  66. [66]

    Paul A Kirschner, John Sweller, Richard E Clark, PA Kirschner, and RE Clark. 2010. Why minimal guidance during instruction does not work: An analysis of the failure of constructivist.Based Teaching Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching,(November 2014)(2010), 37–41

    2010

  67. [67]

    Eva Knekta, Christopher Runyon, and Sarah Eddy. 2019. One size doesn’t fit all: Using factor analysis to gather validity evidence when using surveys in your research.CBE—Life Sciences Education18, 1 (2019), rm1

    2019

  68. [68]

    Christopher Knievel, Alexander Bernhardt, and Christian Bernhardt. 2025. AITEE–Agentic Tutor for Electrical Engineering.arXiv preprint arXiv:2505.21582(2025)

    Pith/arXiv arXiv 2025

  69. [69]

    Michael J Kofler, Leah J Singh, Elia F Soto, Elizabeth SM Chan, Caroline E Miller, Sherelle L Harmon, and Jamie A Spiegel. 2020. Working memory and short-term memory deficits in ADHD: A bifactor modeling approach.Neuropsy- chology34, 6 (2020), 686

    2020

  70. [70]

    Lasha Labadze, Maya Grigolia, and Lela Machaidze. 2023. Role of AI chatbots in education: systematic literature review.International journal of Educational Technology in Higher education20, 1 (2023), 56. 26 Shao et al

    2023

  71. [71]

    Himanshi Lalwani, Maha Elgarf, and Hanan Salam. 2024. Productivity coachbot: a social robot coach for university students with adhd. InA3DE, ACM/IEEE International Conference on Human-Robot Interaction. IEEE

    2024

  72. [72]

    Himanshi Lalwani, Mira Saleh, and Hanan Salam. 2025. A study companion for productivity: exploring the role of a social robot for college students with ADHD. In2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 1438–1442

    2025

  73. [73]

    Min Lan and Xiaofeng Zhou. 2025. A qualitative systematic review on AI empowered self-regulated learning in higher education.npj Science of Learning10, 1 (2025), 21

    2025

  74. [74]

    Anna Lieb and Toshali Goel. 2024. Student interaction with newtbot: An llm-as-tutor chatbot for secondary physics education. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–8

    2024

  75. [75]

    Guan-Chyun Lin, Zhonglin Wen, Herbert W Marsh, and Huey-Shyan Lin. 2010. Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies.Structural Equation Modeling17, 3 (2010), 374–391

    2010

  76. [76]

    Chung Kwan Lo, Khe Foon Hew, and Morris Siu-yung Jong. 2024. The influence of ChatGPT on student engagement: A systematic review and future research agenda.Computers & Education219 (2024), 105100

    2024

  77. [77]

    Like Having a Really Bad PA

    Ewa Luger and Abigail Sellen. 2016. " Like Having a Really Bad PA" The Gulf between User Expectation and Experience of Conversational Agents. InProceedings of the 2016 CHI conference on human factors in computing systems. 5286–5297

    2016

  78. [78]

    Jihao Luo, Chenxu Zheng, Jiamin Yin, and Hock Hai Teo. 2025. Design and assessment of AI-based learning tools in higher education: a systematic review.International Journal of Educational Technology in Higher Education22, 1 (2025), 42

    2025

  79. [79]

    Shuai Ma, Junling Wang, Yuanhao Zhang, Xiaojuan Ma, and April Yi Wang. 2025. Dbox: Scaffolding algorithmic programming learning through learner-llm co-decomposition. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–20

    2025

  80. [80]

    Herbert W Marsh, Kit-Tai Hau, and Zhonglin Wen. 2004. In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural equation modeling11, 3 (2004), 320–341

    2004

Showing first 80 references.