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Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education

T0 review · 4 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Instructors can simultaneously trust and distrust generative AI, a survey of 178 U.S. faculty suggests.

desk verdict A solid mixed-methods study of instructor trust/distrust in GenAI that overreaches in its quantitative claim of coexistence; the qualitative data is the real contribution. read the letter →

arxiv 2502.05770 v1 pith:2YPMSQBP submitted 2025-02-09 cs.HC cs.CY

Wenhan Lyu , Shuang Zhang , Tingting (Rachel) Chung , Yifan Sun , Yixuan Zhang This is my paper · ORCID
classification cs.HCcs.CY
keywords generativeAIinstructortrustdistrusthighereducationcalibrationmixed-methodssurveyfacultyperceptions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports a mixed-methods study of 178 instructors at one U.S. university, conducted in March 2024, and argues that trust and distrust in generative AI (GenAI) are not opposite ends of a single scale but related yet distinct attitudes that can coexist in the same person. The central empirical claim is that high trust does not imply low distrust, and vice versa; the authors demonstrate this by splitting respondents into four groups by median trust and distrust scores and by coding open-ended responses. If correct, the finding shifts the design goal for AI support in teaching: instead of simply increasing instructors' trust in GenAI, institutions and tool designers should aim for calibrated trust, meaning appropriately matched levels of both trust and distrust. The paper also documents a gap between instructors' self-reported familiarity with GenAI and their actual use of it for instructional tasks, and identifies qualitative themes of distrust (social justice, environment, ethics, workload) and trust (transformative potential, 'trust, but verify' practices).

What carries the argument

The central machinery is a survey instrument with twelve Likert-scale items: six measuring trust (competence, personalization, adaptability, anticipation, transformation, enrichment) and six measuring distrust (malevolence, dishonesty, skepticism, inaccuracy, restriction, demotivation). Factor analysis confirms these load on two distinct factors, supporting the treatment of trust and distrust as separate dimensions; the four-group median split (high/low trust × high/low distrust) then operationalizes coexistence. The qualitative half uses the general inductive approach to code 294 open-ended responses into themes of trust and distrust, with 'blind distrust' identified when rejection is stated without direct experience or explicit reasons.

What would settle it

Re-analyze the publicly available raw data with a latent profile analysis or confirmatory factor model that compares the fit of a two-factor (trust and distrust separate) solution against a single bipolar dimension; the coexistence claim weakens if the high-trust–high-distrust cell vanishes when acquiescence bias or response-style effects are controlled. A second check: interview a subsample of the high-trust–high-distrust instructors to see whether their stated reasons for distrust are actually grounded in direct experience, which would contradict the 'blind distrust' interpretation.

Watch

Extended reading notes

Core claim

The paper's central claim is that, for instructors in higher education, trust and distrust in GenAI are related yet distinct constructs that may co-exist: a person can simultaneously hold high trust that GenAI can improve teaching and high distrust that it harms learning, integrity, or society. The authors establish this through a factor analysis showing six trust items and six distrust items load on two separate factors (with Cronbach's α of 0.924 and 0.860), a median-split plotting of instructors into four groups including a high-trust–high-distrust group of 55 (30.9%), and qualitative coding showing that instructors like P57 express both confidence in GenAI's factual outputs and concern about student misuse. The paper further shows that familiarity with GenAI differs significantly across these trust–distrust groups, with higher trust associated with higher familiarity, and that teaching level (undergraduate versus graduate) significantly relates to trust and distrust levels. Based on these results, the paper proposes that institutions should support (dis)trust calibration rather than trust maximization, and it outlines design implications such as sandbox experimentation platforms, storytelling features, and training programs that address root causes of blind trust and blind distrust.

Load-bearing premise

The load-bearing premise is that splitting instructors into four groups at the median trust and distrust scores reveals genuine psychological states rather than an artifact of two imperfectly correlated scales, and that open-ended rejections such as 'Generative AI does not belong here' are evidence of blind distrust rather than an unstated, experience-based judgment.

Editorial extensions

If this is right

  • Instructor-facing AI support should target calibrated trust, pairing trust-building with tools that justify appropriate skepticism, rather than merely raising overall trust levels.
  • Distrust-reduction cannot be assumed to follow from trust-building interventions; the two constructs may need separate interventions.
  • Familiarity with GenAI does not automatically translate into classroom use; most surveyed instructors report moderate-to-high familiarity yet rarely use GenAI for direct instructional tasks, indicating a 'knowing-doing gap'.
  • Undergraduate-only instructors show the lowest trust and highest distrust, suggesting that support efforts may need to be tailored by teaching level.
  • Instructors who exhibit 'blind distrust' may require different engagement strategies, because they reject GenAI outright without direct experience or stated reasons.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If trust and distrust are orthogonal, then attitude surveys that only measure trust will systematically misestimate the proportion of instructors who are genuinely wary, because a substantial group may score high on both.
  • The four-group typology suggests a testable hypothesis: instructors in the high-trust–high-distrust cell are likely the best candidates for 'trust, but verify' behaviors, since they hold both the motivation to use GenAI and the vigilance to check its output.
  • A longitudinal extension of this design could test whether the coexistence of trust and distrust is a transient state during an adoption period or a stable attitude structure; if stable, calibration interventions would need to maintain distrust, not eliminate it.
  • The same factor-analytic approach could be applied to students, whose trust and distrust dynamics may differ from instructors and could affect how they respond to AI policies.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper reports a mixed-methods survey of 178 instructors at a single U.S. university, examining their practices, familiarity, trust, and distrust regarding generative AI (GenAI) in March 2024. The quantitative component includes descriptive statistics, factor analysis of trust and distrust items, median-split group comparisons, and Welch's ANOVA across teaching levels and familiarity; the qualitative component uses open-ended responses with a general inductive approach. The central claim is that trust and distrust in GenAI are related yet distinct constructs that can co-exist among instructors, with implications for calibrating both rather than simply increasing trust. The paper also reports that familiarity with GenAI is higher in high-trust groups, and that trust and distrust differ by undergraduate/graduate teaching level. The authors propose design implications for platform design, institutional support, and training to foster calibrated (dis)trust.

Significance. The topic is timely and important for HCI and higher-education research. The mixed-methods design and the focus on instructors (rather than only students) are strengths, as is the public availability of anonymized survey materials, data, and analysis code on OSF. If the coexistence claim is supported, the practical implications for faculty support and AI literacy programs are meaningful. However, the quantitative evidence for the central claim is currently vulnerable to methodological artifacts: the factor analysis is confounded by item valence, and the four-group taxonomy is mechanically produced by median splits. The qualitative data, especially the 'trust, but verify' examples, provide some support for coexistence, but the quantitative foundation needs strengthening before the central claim can be considered established.

major comments (4)
  1. [Section 4.2.1, Table 3] The two-factor exploratory factor analysis does not provide sufficient evidence that trust and distrust are distinct, because all six trust items are positively worded benefits and all six distrust items are negatively worded risks. With Likert agreement scales, a general acquiescence or disacquiescence response style can create separate factors and a negative inter-factor correlation even if the underlying attitude is a single bipolar dimension. The authors should report a confirmatory factor analysis comparing a two-factor model against a one-factor model (or a one-factor model with a method factor), and provide discriminant validity evidence such as Fornell-Larcker or HTMT. Without such tests, the factor-analytic support for the 'related yet distinct' claim is not established.
  2. [Section 4.2.2, Figure 5] The four-group taxonomy is generated by median splits on the trust and distrust scales. For any two imperfectly correlated variables, median splits will populate all four quadrants, so the existence of a high-trust-high-distrust cell (n=55) is a mechanical consequence of the splitting procedure and does not by itself demonstrate that trust and distrust coexist as psychological states. To support the coexistence claim, the authors should use a method that is not an artifact of central splitting, for example, comparing the observed proportion of high-high respondents against the proportion expected under a bipolar model with measurement error, applying latent profile analysis, or reporting the number of respondents who score above a priori meaningful thresholds on both scales.
  3. [Section 5.1.1] The inference of 'blind distrust' is underjustified. The authors classify P31, P79, and P16 as blind distrust because their open-ended responses do not articulate reasons, but the survey prompt asked for examples and concerns rather than a justification of attitudes. An absence of a stated reason in a short response is not evidence of the absence of a reason. The label 'blind distrust' should be supported by a defined coding rule (e.g., an explicit statement that the respondent has no experience or cannot give a reason), or the interpretation should be softened to something like 'strongly asserted distrust without elaborated justification in the response.'
  4. [Section 6.2.4] The paper itself acknowledges in Section 6.2.4 that distrust may be context-specific and that some responses might reflect ideological positions rather than blanket rejection. This qualification is in tension with the earlier interpretation of the same responses as 'blind distrust' in Section 5.1.1. The authors should either apply this nuance when presenting the qualitative results or revise the earlier section to acknowledge that the open-ended data cannot distinguish between blind distrust and principled, experience-based rejection without further probing.
minor comments (6)
  1. [Section 3.3] The factor analysis description does not report the extraction method (e.g., principal axis factoring vs. maximum likelihood) or the rotation method (e.g., varimax vs. promax), which is needed for reproducibility.
  2. [Figure 4 caption] The caption states that the color gradient from blue to white represents increasing positive correlations, with white indicating no or negative correlation, but the text is ambiguous about how negative correlations are shown; please clarify the color scale.
  3. [Figure 6] The figure includes an 'Other' group (n=32), but the text does not explain what this group represents; please define it in Section 4.2.2 or in the figure caption.
  4. [Section 4.2.3] The discussion of familiarity differences uses causal language such as 'building high trust may help them become more familiar,' but the cross-sectional design cannot support causal claims; please temper these statements.
  5. [Section 3.1] Please report the response rate explicitly (219 responses from approximately 1000 invited, 178 complete) and briefly discuss possible non-response bias, given the relatively low completion rate.
  6. [Table 2] The Distrust dimension 'Dishonesty' is described in the text as 'a potential source of misinformation,' but the statement wording in Table 2 is 'GenAI is a potential source of misinformation for students in my courses'; please align the dimension label with the item content to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the coexistence claim is an empirical finding, not a derivation that reduces to its inputs.

full rationale

The paper's central claim—that trust and distrust in GenAI are related yet distinct and can co-exist—rests on empirical correlations, an exploratory factor analysis, group comparisons, and qualitative responses. The trust and distrust items in Table 2 are selected from prior literature and differ in valence, but the factor loadings in Table 3 and the four-group median split in Figure 5 are data-dependent outputs; item selection alone does not force the reported two-factor solution or guarantee the existence of a high-trust-high-distrust cell. The qualitative findings in Section 5 independently quote instructors expressing both trust and distrust. The main risks to the claim, such as a possible item-valence/acquiescence confound and the absence of explicit discriminant-validity tests, are methodological validity threats rather than circular reductions. The self-citations ([74], [109], [125]) appear only as background or related-work support and are not load-bearing for the coexistence conclusion. No step in the paper's argument equals its input by construction.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities such as particles, forces, or mediators. It does rely on several domain assumptions about measurement validity and interpretation of open-ended text, plus one grouping parameter (median split) that is central to the coexistence claim.

free parameters (1)
  • Median split thresholds for trust and distrust = Sample medians, values not reported in the paper
    Used in Section 4.2.2 to define high-trust-high-distrust, high-trust-low-distrust, low-trust-high-distrust, and low-trust-low-distrust groups. The sizes and even the existence of these cells depend on the chosen cutoffs, making the threshold choice central to the coexistence claim.
assumptions (4)
  • domain assumption The six trust items and six distrust items in Table 2 validly operationalize the trust and distrust constructs.
    Invoked in Section 3.2.2 and supported only by exploratory factor analysis on the same data; no confirmatory factor analysis or discriminant validity test is reported.
  • domain assumption Self-reported familiarity with GenAI is a valid proxy for informedness about GenAI capabilities and limitations.
    Used in Section 4.2.3 to interpret familiarity differences across trust and distrust groups as evidence about blind trust and blind distrust.
  • ad hoc to paper Absence of a written justification in an open-ended response is evidence of blind distrust.
    Underlies the blind-distrust theme in Section 5.1.1, where quotes without explicit reasons are labeled as blind distrust without direct evidence of the respondent's reasoning.
  • domain assumption The 178 responding instructors are treated as informative about U.S. higher education instructors generally.
    The title and abstract generalize to U.S. higher education, while Section 7 acknowledges that the single-institution sample limits generalizability.

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Cite this review

Pith. "Pith review of Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education." pith.science (2026). https://pith.science/paper/2YPMSQBP

@misc{pith2026250205770,
  author       = {Pith},
  title        = {Pith review of: Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2YPMSQBP}},
  note         = {Machine review of arXiv:2502.05770}
}
read the original abstract

Generative AI (GenAI) has brought opportunities and challenges for higher education as it integrates into teaching and learning environments. As instructors navigate this new landscape, understanding their engagement with and attitudes toward GenAI is crucial. We surveyed 178 instructors from a single U.S. university to examine their current practices, perceptions, trust, and distrust of GenAI in higher education in March 2024. While most surveyed instructors reported moderate to high familiarity with GenAI-related concepts, their actual use of GenAI tools for direct instructional tasks remained limited. Our quantitative results show that trust and distrust in GenAI are related yet distinct; high trust does not necessarily imply low distrust, and vice versa. We also found significant differences in surveyed instructors' familiarity with GenAI across different trust and distrust groups. Our qualitative results show nuanced manifestations of trust and distrust among surveyed instructors and various approaches to support calibrated trust in GenAI. We discuss practical implications focused on (dis)trust calibration among instructors.

Figures

Figures reproduced from arXiv: 2502.05770 by the authors.

Figure 1
Figure 1. Response distribution of current practices and future intentions regarding the use of GenAI tools for instruction, [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Response distribution of the types of instructional tasks for which instructors typically use GenAI tools, ordered by [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Instructors’ attitudes towards training in GenAI, ordered by the percentage of responses in the most frequent category [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Correlation matrix between all items for the trust [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 6
Figure 6. Figure 6: Differences in familiarity with GenAI across differ [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Differences in (A) trust and (B) distrust across different teaching levels (undergraduate vs. graduate level). [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

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Cited by 1 Pith paper

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.