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arxiv: 2605.18140 · v1 · pith:CDO7YYVMnew · submitted 2026-05-18 · ⚛️ physics.ed-ph · cs.CY

Faculty Orientations Shape Adoption of AI in Research and Teaching

Timothy J. Atherton , Ian Descamps , Tova R. Holmes , Christina L. Vizcarra , Ning Sui , Max Webel , Jay J. Foley IV This is my paper

Pith reviewed 2026-05-20 00:01 UTC · model grok-4.3

classification ⚛️ physics.ed-ph cs.CY
keywords AI adoptionfaculty orientationspedagogical orientationSTEM educationexploratory factor analysistechnology integrationdisciplinary thinking
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The pith

Faculty orientations toward AI's role in disciplinary thinking strongly predict their adoption of AI tools in research and teaching.

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

A mixed-methods survey of 90 STEM faculty identified a coherent construct called AI pedagogical orientation that strongly predicts self-reported AI use across research, teaching, and other activities. This orientation reflects differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use. The results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary reasoning and knowledge production.

Core claim

Exploratory factor analysis identified a coherent construct, AI pedagogical orientation, that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated that this construct reflected differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use.

What carries the argument

The AI pedagogical orientation construct, identified via exploratory factor analysis, which captures faculty beliefs about how AI should integrate into disciplinary thinking, learning, and expertise development.

If this is right

  • AI adoption depends more on specific beliefs about its place in disciplinary reasoning than on general attitudes or external incentives.
  • Standard technology-adoption frameworks may require updates for tools that directly shape knowledge production.
  • Promotion of AI use should target alignment with faculty views on expertise development rather than broad institutional mandates.
  • Differences in AI integration mirror underlying differences in how faculty conceptualize learning and thinking in their fields.

Where Pith is reading between the lines

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

  • The same orientation construct could be tested for presence and predictive power among faculty in non-STEM disciplines.
  • Professional development might usefully include explicit discussion of AI's appropriate role in disciplinary expertise.
  • Longitudinal tracking could reveal whether these orientations shift over time with increased AI exposure or training.
  • The result raises questions about whether AI tools will ultimately alter the core practices that define expertise in various fields.

Load-bearing premise

The factor structure derived from this sample of 90 faculty in one professional community reflects a stable, generalizable orientation rather than sample-specific response patterns or self-report bias.

What would settle it

A larger replication survey across diverse faculty populations that fails to recover the same coherent factor or finds no strong predictive link to AI use would undermine the central claim.

Figures

Figures reproduced from arXiv: 2605.18140 by Christina L. Vizcarra, Ian Descamps, Jay J. Foley IV, Max Webel, Ning Sui, Timothy J. Atherton, Tova R. Holmes.

Figure 1
Figure 1. Figure 1: AI usage. (a) Number of respondents reporting use of different categories of AI tools for research (black), teaching (light grey), and other professional tasks (dark grey). (b) Correlation matrix showing pairwise correlations between binary indicators of AI use in research, teaching, and other domains [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Sources and information about AI tools (a) Number of respondents reporting use of different sources of AI tools. (b) Number of respondents reporting reliance on specific data sources to learn about AI in higher education. anisms were used by only 29 participants (32%), suggest￾ing that institutional initiatives have limited influence of faculty orientation in this cohort. Responses to attitudinal items dis… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of responses to Likert-scale atti￾tudinal statements about AI in teaching and research. For each statement, colored segments indicate response cat￾egories: strongly disagree (red), disagree (orange), neutral (light grey), agree (light blue), and strongly agree (dark blue). The figure shows broadly positive attitudes toward AI’s ped￾agogical value coexisting with notable concerns about its in￾t… view at source ↗
Figure 4
Figure 4. Figure 4: Instructional methods used. Number of re￾spondents reporting various methods in their teaching; these responses are independent of AI usage. (a) Assistant Professor 16 18% Associate Professor 32 36% Full Professor 41 46% N % Liberal arts college 18 20% Primarily undergraduate (PUI) 35 39% Research University 52 58% Minority Serving (MSI) 12 13% Private 20 22% N % State 33 37% Rural 13 14% Urban 21 23% Astr… view at source ↗
Figure 5
Figure 5. Figure 5: Respondent demographic and institutional information. (a) Career stage of respondents. Note due to eligibility requirements of the Cottrell Scholar Award, all respondents are tenured or tenure-track faculty. (b) Respon￾dent’s degree. (c) Respondent’s home department. (d) High￾est degree granted by home department of respondents. (e) Respondent’s institution type. Note that responses were non￾exclusive. usi… view at source ↗
Figure 6
Figure 6. Figure 6: Factor loadings for the “AI pedagogical orientation” factor identified through exploratory factor analysis. Positive loadings (strength of association) indicate alignment with favorable attitudes toward AI use in teaching and learning, while the negative loading reflects concern about integration. Higher-magnitude loadings indicate stronger contributions to AI pedagogical orientation. 0.47), but other vari… view at source ↗
read the original abstract

Despite the widespread availability of large language models (LLMs) in higher education, instructors vary substantially in their adoption and use of these tools, and the reasons for this variation remain poorly understood. A mixed-methods survey of 90 STEM faculty in the Research Corporation for Science Advancement (RCSA) Cottrell community examined relationships between AI use, attitudes, institutional context, and instructional practice. Exploratory factor analysis identified a coherent construct, \textit{AI pedagogical orientation}, that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated that this construct reflected differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use. The results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary reasoning and knowledge production.

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

3 major / 2 minor

Summary. The manuscript reports on a mixed-methods survey of 90 STEM faculty in the RCSA Cottrell community examining relationships between AI use, attitudes, institutional context, and instructional practice. Exploratory factor analysis identified a coherent construct termed AI pedagogical orientation that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated this construct reflects differing views about the role of AI in disciplinary thinking, learning, and expertise development rather than general positive or negative attitudes. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations.

Significance. If the central result holds, the work contributes empirical insight into variation in faculty AI adoption by identifying a discipline-linked orientation construct that extends beyond standard technology-adoption models. The mixed-methods design and focus on an original sample from a professional community provide concrete data on how orientations tied to disciplinary reasoning shape AI integration in research and teaching.

major comments (3)
  1. [Exploratory factor analysis subsection] Exploratory factor analysis subsection: with n=90 the analysis requires explicit reporting of KMO/Bartlett statistics, eigenvalue thresholds or scree criteria, factor loadings, variance explained, and any stability or cross-validation checks. These are absent from the described results, leaving the coherence and generalizability of the AI pedagogical orientation construct unverified and risking sample-specific response patterns.
  2. [Results on predictive relationships] Results on predictive relationships: the claim that the identified construct strongly predicted self-reported AI use lacks reported quantitative support such as correlation coefficients, regression betas, R^{2} values, or effect sizes. Without these metrics the strength of the central association cannot be assessed.
  3. [Measures and data collection] Measures and data collection: both the orientation construct and AI use are measured via self-report with no objective validation, behavioral logs, or discussion of social-desirability bias. This shared-method variance is load-bearing for the reported predictive relationship.
minor comments (2)
  1. [Abstract] Abstract: include the sample size (n=90) and at least one key quantitative detail (e.g., variance explained or a prediction metric) to allow readers to evaluate the strength of the reported findings.
  2. [Discussion] Discussion: provide a more explicit comparison of the AI pedagogical orientation construct to existing technology-adoption frameworks (e.g., TAM or UTAUT) to clarify the claimed extension.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for their constructive and detailed feedback, which identifies key areas where the reporting and discussion in our manuscript can be strengthened. We address each major comment below and note the corresponding revisions.

read point-by-point responses

  1. Referee: [Exploratory factor analysis subsection] Exploratory factor analysis subsection: with n=90 the analysis requires explicit reporting of KMO/Bartlett statistics, eigenvalue thresholds or scree criteria, factor loadings, variance explained, and any stability or cross-validation checks. These are absent from the described results, leaving the coherence and generalizability of the AI pedagogical orientation construct unverified and risking sample-specific response patterns.

    Authors: We agree that these diagnostic statistics and details are essential for readers to evaluate the exploratory factor analysis. We will revise the manuscript to include explicit reporting of the KMO measure, Bartlett's test of sphericity, the eigenvalue thresholds and scree plot criteria used for factor retention, all relevant factor loadings, the variance explained, and results from stability or cross-validation procedures such as split-sample checks. These additions will be placed in the Methods and Results sections to substantiate the coherence of the AI pedagogical orientation construct. revision: yes

  2. Referee: [Results on predictive relationships] Results on predictive relationships: the claim that the identified construct strongly predicted self-reported AI use lacks reported quantitative support such as correlation coefficients, regression betas, R^{2} values, or effect sizes. Without these metrics the strength of the central association cannot be assessed.

    Authors: We acknowledge that the manuscript did not provide sufficient quantitative detail on the predictive models. We will revise the Results section to report the relevant correlation coefficients, regression betas (standardized and unstandardized), R^{2} values, and effect sizes for the associations between AI pedagogical orientation and self-reported AI use across research, teaching, and other activities. These metrics will be added to allow direct assessment of the strength and practical significance of the relationships. revision: yes

  3. Referee: [Measures and data collection] Measures and data collection: both the orientation construct and AI use are measured via self-report with no objective validation, behavioral logs, or discussion of social-desirability bias. This shared-method variance is load-bearing for the reported predictive relationship.

    Authors: This is a valid methodological limitation inherent to the survey design. As data collection is complete, we cannot add new objective validation measures or behavioral logs. We will partially revise the manuscript by expanding the Limitations and Discussion sections to explicitly address reliance on self-report data, potential social-desirability bias, and the implications of shared-method variance for the observed predictive relationships. We will also suggest avenues for future research incorporating objective or multi-method data. revision: partial

Circularity Check

0 steps flagged

Empirical survey with EFA on original data exhibits no circularity

full rationale

This is a mixed-methods empirical survey study reporting exploratory factor analysis on original responses from 90 faculty. The identification of the 'AI pedagogical orientation' construct and its reported predictive relationship to self-reported AI use are derived directly from the collected survey items and qualitative coding without any equations, parameter fits, or self-citation chains that reduce the central claim to its own inputs by construction. The derivation chain is self-contained against external benchmarks as it describes observed statistical patterns and thematic analysis in the sample data.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The central claim rests on the assumption that exploratory factor analysis of self-reported survey items from a convenience sample of 90 faculty yields a meaningful, stable construct that explains adoption behavior beyond existing models.

free parameters (1)
  • Factor structure and loadings
    Determined empirically from the survey responses to define the AI pedagogical orientation construct.
axioms (2)
  • domain assumption Self-reported AI use and attitudes accurately reflect actual behavior and beliefs.
    Standard assumption in survey research but vulnerable to social desirability and recall bias.
  • domain assumption Exploratory factor analysis on this sample size produces replicable factors.
    Common in social science but requires sufficient sample and clear criteria for factor retention.
invented entities (1)
  • AI pedagogical orientation no independent evidence
    purpose: To account for variation in AI adoption not explained by general attitudes or institutional factors.
    Construct extracted via factor analysis of this dataset; no independent falsifiable test outside the survey is described.

pith-pipeline@v0.9.0 · 5715 in / 1377 out tokens · 59365 ms · 2026-05-20T00:01:25.406178+00:00 · methodology

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