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Cited by 226Artificial Intelligence in EducationTeaching and Learning ProgrammingGender and Technology in Education

An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence

Open access
Chai, Ching Sing, Wang, Xingwei, Xu, Chang·2020·Mathematics
DOI 10.3390/math8112089
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Abstract

Artificial Intelligence (AI) is currently changing how people live and work. Its importance has prompted educators to begin teaching AI in secondary schools. This study examined how Chinese secondary school students’ intention to learn AI were associated with eight other relevant psychological factors. Five hundred and forty-five secondary school students who have completed at least one cycle of AI course were recruited to participate in this study. Based on the theory of planned behavior, the students’ AI literacy, subjective norms, and anxiety were identified as background factors. These background factors were hypothesized to influence the students’ attitudes towards AI, their perceived behavioral control, and their intention to learn AI. To provide more nuanced understanding, the students’ attitude towards AI was further delineated as constituted by their perception of the usefulness of AI, the potential of AI technology to promote social good, and their attitude towards using AI technology. Similarly, the perceived behavioral control was operationalized as students’ confidence towards learning AI knowledge and optimistic outlook of an AI infused world. Relationships between the factors were theoretically illustrated as a model that depicts how students’ intention to learn AI was constituted. Two research questions were then formulated. Confirmatory factor analysis was employed to validate that multi-factor survey, followed by structural equational modelling to ascertain the significant associations between the factors. The confirmatory factor analysis supports the construct validity of the questionnaire. Twenty-five out of the thirty-three hypotheses were supported through structural equation modelling. The model helps researchers and educators to understand the factors that shape students’ intention to learn AI. These factors should be considered for the design of AI curriculum.

AI snapshot

Written from the paper’s full text.

TL;DR

This study tested an extended theory of planned behavior model of Chinese secondary school students’ intention to learn AI using a 9-factor survey and structural equation modelling. Confirmatory factor analysis supported the survey, and 25 of 33 hypothesized paths were supported, with intention directly predicted by perceived usefulness, AI for social good, attitude toward using AI, confidence, and AI optimism.

Key findings

  • The 9-factor survey was validated: 37 items were retained (factor loadings 0.674–0.873), all subscales had CR > 0.70 and AVE > 0.50, and overall Cronbach’s alpha was 0.92; CFA fit was p = 0.000, χ²/df = 1.448, RMSEA = 0.029, SRMR = 0.027, GFI = 0.92, TLI = 0.97, CFI = 0.98.
  • SEM supported 25 of 33 hypotheses; model fit was p = 0.000, χ²/df = 1.451, RMSEA = 0.029, SRMR = 0.044, GFI = 0.92, TLI = 0.97, CFI = 0.98.
  • AI literacy and subjective norms significantly predicted perceived usefulness, AI for social good, attitude toward using AI, confidence, and AI optimism (H1–H5, H7–H11), but did not directly predict behavioral intention (H6, H12 unsupported).
  • Behavioral intention was directly predicted by perceived usefulness (H22), AI for social good (H27), attitude toward using AI (H29), confidence (H32), and AI optimism (H33); Table 3 shows AI anxiety predicted AI for social good (H14), attitude toward using AI (H15), AI optimism (H17), and behavioral intention (H18), but not perceived usefulness (H13) or confidence (H16).

Methods

  • Purposeful sample of 545 Chinese secondary students (56.33% male; age 13–18, M = 14, SD = 1.30) who had completed at least one AI course; 37.43% were from Zhengzhou, 33.76% from Qingdao, and 28.80% from Shanghai; all were in an afterschool program, with >30 h of AI learning and 47.89% reporting >100 h.
  • Instrument: 9 constructs and 41 items originally, answered on a six-point Likert scale; CFA then SEM were performed in AMOS 22; normality checks showed skewness −0.911 to 0.282, kurtosis −0.962 to 0.147, and Mardia’s coefficient 294.28.
  • Analysis: CFA validated the measurement model, followed by SEM to test H1–H33; fit criteria included p < 0.001, χ²/df < 3, RMSEA < 0.08, SRMR < 0.05, and GFI/TLI/CFI > 0.90.

Limitations

  • Stated: the limited sample went through a similar AI curriculum, so findings may not generalize to students with other AI learning experiences; only psychological factors were included, not facilitating conditions such as learning space and equipment; data were self-reported, without objective learning outcomes; 33 hypotheses were tested and only 15 were supported at p < 0.001, so other supported hypotheses may suffer from Type I error and results at p < 0.05–0.01 should be interpreted carefully.
  • Evident from the text: purposive sampling came from one afterschool program provided by one of the researchers, which may introduce selection bias and further limit generalizability; the design appears cross-sectional (survey at the end of the course), so the directional SEM paths are theoretical rather than experimentally established.

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Registry

Crossref · 10.3390/math8112089

Cites

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Statistics

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Claims the paper makes

0 of 33 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Subjective norms did not directly predict behavioral intention to learn AI.

    “However, AI literacy and subjective norms could not directly predict behavioral intention to learn AI; H6 and H12 were unsupported.”
    Quote in the paperSubstantial claim
  • Findingown data

    AI anxiety significantly predicted attitude toward using AI.

    “AI anxiety significantly predicted AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism; H14, H15, H17, and H18 were supported.”
    Quote in the paperSubstantial claim
  • Findingown data

    Perceived usefulness of AI significantly predicted confidence in learning AI.

    “Perceived usefulness of AI significantly predicted attitude toward use AI, confidence in learning AI, AI optimism, and behavioral intention to learn AI; H20-23 were supported.”
    Quote in the paperSubstantial claim
  • Findingown data

    Perceived usefulness of AI significantly predicted behavioral intention to learn AI.

    “Perceived usefulness of AI significantly predicted attitude toward use AI, confidence in learning AI, AI optimism, and behavioral intention to learn AI; H20-23 were supported.”
    Quote in the paperSubstantial claim
  • Findingown data

    AI anxiety significantly predicted AI for social good.

    “AI anxiety significantly predicted AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism; H14, H15, H17, and H18 were supported.”
    Quote in the paperSubstantial claim
  • May combine two claims. 29
  • Classified as probably not a substantial claim. 16
  • The quote only partly states this claim. 2

  • May combine two claims.

    Findingown data

    Students were optimistic and not too anxious about the use of AI.

    “They were optimistic and not too anxious about the use of AI, and they wished to learn more about AI.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    AI optimism significantly predicted behavioral intention to learn AI (H33 supported).

    “AI optimism significantly predicted behavioral intention to learn AI; H33 was supported.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    AI anxiety did not directly predict perceived usefulness of AI and confidence in learning AI (H13 and H16 unsupported).

    “However, AI anxiety could not directly predict perceived usefulness of AI and confidence in learning AI; H13 and H16 were unsupported.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Perceived usefulness of AI significantly predicted attitude toward using AI, confidence in learning AI, AI optimism, and behavioral intention to learn AI (H20-H23 supported).

    “Perceived usefulness of AI significantly predicted attitude toward use AI, confidence in learning AI, AI optimism, and behavioral intention to learn AI; H20-23 were supported.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    AI literacy significantly predicted perceived usefulness of AI, AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism (H1-H5 supported).

    “AI literacy and subjective norms significantly predicted perceived usefulness of AI, AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism; H1-H5 and H7-H11 were supported.”
    • Also stated in: § 5. Discussion and Conclusions · plain
      “Among the background factors, AI literacy is positively associated with all the attitudes towards the behavior and perceived behavioral control factors but not directly influencing behavioral intention.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Subjective norms significantly predicted perceived usefulness of AI, AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism (H7-H11 supported).

    “AI literacy and subjective norms significantly predicted perceived usefulness of AI, AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism; H1-H5 and H7-H11 were supported.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Attitude toward using AI did not directly predict confidence in learning AI and AI optimism (H28 and H30 unsupported).

    “However, attitude toward using AI could not directly predict confidence in learning AI and AI optimism; H28 and H30 were unsupported.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Students were almost neutral about their confidence to learn AI and their knowledge about AI.

    “Nonetheless, they were almost neutral about their confidence to learn AI and their knowledge about AI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Thirty-three hypotheses were tested in this study and 25 were accepted.

    “Finally, 33 hypotheses were tested in this study, and 25 hypotheses were accepted.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    AI anxiety significantly predicted AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism (H14, H15, H17 and H18 supported).

    “AI anxiety significantly predicted AI for social good, attitude toward using AI, confidence in learning AI, and AI optimism; H14, H15, H17, and H18 were supported.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Confidence in learning AI positively influences students' readiness.

    “Confidence in learning AI has been reported to positively influence students' readiness [2].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] Chai, C.S., Lin, P.Y., Jong, M.S.Y., Dai, Y., Chiu, T.K.F., and Qin, J.J. (2020). Primary school students’ perceptions and behavioral intentions of learning artificial intelligence. Educ. Technol. Soc., in press.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Chinese students' AI literacy does not predict their intention to learn directly but through the factor of social good.

    “More importantly, a recent study has revealed that Chinese students' AI literacy does not predict their intention to learn directly but through the factor of social good [2].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] Chai, C.S., Lin, P.Y., Jong, M.S.Y., Dai, Y., Chiu, T.K.F., and Qin, J.J. (2020). Primary school students’ perceptions and behavioral intentions of learning artificial intelligence. Educ. Technol. Soc., in press.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    AI literacy predicts students' confidence, perceived relevance of learning AI, and readiness towards AI.

    “AI literacy has thus been reported to predict students' confidence, perceived relevance of learning AI and readiness towards AI [15].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [15] Dai, Y., Chai, C.S., Lin, P.Y., Jong, S.Y., Guo, Y., and Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12. source

      Not checked in the free run.

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    Premisecitation

    Social influence shapes learners' attitudes towards the behavior and behavioral intention to learn in technology-enhanced learning.

    “It has been shown to be a precedent factor that shapes learners' attitudes towards the behavior and behavioral intention to learn in the context of technology-enhanced learning [16,17].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [16] Botero, 2018, Acceptance and usage of mobile assisted language learning by higher education students, J. Comput. High. Educ. source

      Not checked in the free run.

    • cites [17] Hoi, 2020, Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach, Comput. Educ. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Confidence positively influences students' continuous intention to learn.

    “Repeated findings point to confidence as a positively influence of students' continuous intention to learn [37,38] (i.e., H32).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [37] Garland, 2005, Attitude and confidence towards computers and books as learning tools: A cross-sectional study of student cohorts, Br. J. Educ. Technol. source

      Not checked in the free run.

    • cites [38] Lee, 2010, Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model, Comput. Educ. source

      Not checked in the free run.

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    Premisecitation

    Perceived usefulness often predicts attitude towards use and user intention to use technology.

    “In the technology acceptance model literature, perceived usefulness often predicts attitude towards use and user intention to use technology [32][33][34].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32][33][34]

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  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    A student's AI knowledge informs her confidence and shapes the attitude.

    “While it has been reported that a student's AI knowledge informs her confidence and shapes the attitude [15,41], the direction of influence between perceived behavioral control and attitude towards use could be contextual.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [15] Dai, Y., Chai, C.S., Lin, P.Y., Jong, S.Y., Guo, Y., and Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12. source

      Not checked in the free run.

    • cites [41] Yildiz, 2018, Flipped learning readiness in teaching programming in middle schools: Modelling its relation to various variables, J. Comput. Assist. Learn. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Anxiety may not influence children's sense of readiness, and children are self-reportedly not anxious about but optimistic towards AI.

    “Earlier research indicated that anxiety may not influence children's sense of readiness, and they are self-reportedly not anxious about but optimistic towards AI [15].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [15] Dai, Y., Chai, C.S., Lin, P.Y., Jong, S.Y., Guo, Y., and Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Social good predicts behavioral intention.

    “A previous study has reported that social good predicts behavioral intention [2].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] Chai, C.S., Lin, P.Y., Jong, M.S.Y., Dai, Y., Chiu, T.K.F., and Qin, J.J. (2020). Primary school students’ perceptions and behavioral intentions of learning artificial intelligence. Educ. Technol. Soc., in press.

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    The sub-scales had satisfactory reliability and convergent validity (CR > 0.70, AVE > 0.50).

    “the examination of the composite reliability (CR) of each sub-scale was greater than 0.70, and the average variance extracted (AVE) exceeded the value of 0.50”
    • Also stated in: § 4.1. Analysis of the Measurement Model · hedged
      “indicating satisfactory reliability and convergent validity of each sub-scale”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Social influence has significant effects on participants' intention to use AI products.

    “Sohn and Kwon [18] reported that social influence represented in theoretical models such as theory of planned behavior and the Unified Theory of Acceptance and Use of Technology (UTAUT) has significant effects on the participants' intention to use AI products.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Sohn, 2020, Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products, Telemat. Inform. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    When an action is perceived to produce positive results, be socially well accepted, and be perceived as within one's locus of control, strong behavioral intention is likely to be formed, and this is predictive of actual behavior.

    “The general findings reported in numerous studies employing the theory of planned behavior indicate that when an action is perceived to (a) produce positive results, (b) be socially well accepted (i.e., conforming to subjective norms), and (c) be perceived as within one's locus of control, strong behavioral intention is likely to be formed, and this is predictive of actual behavior [10].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [10] Liao, 2007, Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model, Comput. Hum. Behav. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Illustrating the usefulness and enjoyment of AI could promote students' perceived behavioral control (confidence and optimism), which reinforces their intention to learn.

    “As indicated by our findings, illustrating the usefulness and enjoyment could also promote students' perceived behavioral control (i.e., confidence and optimism) that reinforces their intention to learn.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    With a well-designed curriculum that emphasizes usefulness for the common good, secondary students can be confident and optimistic, with their appropriate intention to learn AI enhanced.

    “With a well-designed curriculum that emphasizes usefulness for common good [25,26], secondary students can be confident and optimistic, with their appropriate intention to learn AI enhanced.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [25] Bryson, 2017, Standardizing ethical design for artificial intelligence and autonomous systems, Computer source

      Not checked in the free run.

    • cites [26] Floridi, 2018, AI4People—An ethical framework for a Good AI Society: Opportunities, risks, principles, and recommendations, Minds Mach. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    Subjective norms could be interpreted as a general social expectation that forms the background for one to view AI as an emerging technological phenomenon.

    “As argued, subjective norms could be interpreted as a general social expectation that forms the background for one to view AI as an emerging technological phenomenon.”
    Quote in the paperNot a substantial claim

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    Study design and conductcitation

    The structural equation model fitted the data well, with p = 0.000, χ2/df = 1.451, RMSEA = 0.029, SRMR = 0.044, GFI = 0.92, TLI = 0.97 and CFI = 0.98.

    “The indices indicated that the SEM model had a good fit: p = 0.000 (<0.001), χ 2 /df = 1.451 (<3.0),RMSEA = 0.029 (<0.08),SRMR = 0.044 (<0.05),GFI = 0.92 (>0.90),TLI = 0.97 (>0.90),CFI = 0.98 (>0.90) [43].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [43] Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2010). Multivariate Data Analysis, Prentice Hall. [7th ed.].

      Not checked in the free run.

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    Premisecitation

    In a previous study, AI anxiety was not an issue among primary school students and was not correlated with factors such as students' sense of readiness, relevance of learning AI, and confidence in learning AI.

    “A previous study on AI anxiety indicates that it is not an issue among primary school students and that the factor is not correlated to other factors like students' sense of readiness, relevance of learning AI, and their confidence in learning AI [15].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [15] Dai, Y., Chai, C.S., Lin, P.Y., Jong, S.Y., Guo, Y., and Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    This study uses self-reported data.

    “Thirdly, this study uses self-reported data.”
    Quote in the paperNot a substantial claim

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    Interpretationown data

    AI anxiety is negatively associated with students' desire to design AI for social good and to learn AI, and negatively associated with optimism and attitude toward use.

    “It seems that AI anxiety is negatively associated with students' desire to design AI for social good and to learn AI; and it is negatively associated with optimism and attitude towards use.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    The survey constructed in this study is valid and reliable and may be used to study secondary students' learning of AI.

    “Overall, the findings support the survey constructed is valid and reliable and may be used to study secondary students' learning of AI.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Fishbein and Ajzen put subjective norms at the same level as attitudes towards the behavior and perceived behavioral control.

    “While Fishbein and Ajzen [7] put subjective norms at the same level as attitudes towards the behavior and perceived behavioral control, this study found that the factors used to denote attitudes towards the behavior and perceived behavioral control are predicted by subjective norms.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [7] Fishbein, M., and Ajzen, I. (2010). Predicting and Changing Behavior: The Reasoned Action Approach, Psychology Press. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The results indicate that the learning experiences were positive although there is apparently room to foster stronger intention to learn.

    “Overall, the results seem to fit with novices' perspective, and they seem to indicate that the learning experiences were positive although there is apparently room to foster stronger intention to learn.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Secondary students' intention to learn AI is influenced directly by AI anxiety, perceived usefulness, social good, attitude towards use, confidence and optimism, and indirectly by AI literacy and subjective norms.

    “and it is influenced directly by AI anxiety, perceived usefulness, social good, attitude towards use, confidence and optimism and indirectly by AI literacy and subjective norms.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Study design and conductreasoning

    The supported hypotheses at the level between p < 0.05 and p < 0.01 have to be carefully interpreted.

    “The supported hypotheses at the level between p < 0.05 and p < 0.01 have to be carefully interpreted.”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Study design and conductown data

    Only 15 hypotheses were supported at the level of p < 0.001, and the rest of the supported hypotheses may suffer from type I error.

    “The rest of the supported hypotheses may suffer from type I error.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    China's secondary schools are starting to experiment with the teaching of AI in response to government policy.

    “In other words, China's secondary schools are starting to experiment with the teaching of AI in response to government policy [3].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [3] Knox, J. (2020). Artificial intelligence and education in China. Learn. Media Technol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Confirmatory factor analysis was employed to assess the construct validity of the survey, whose items were adopted or adapted with minor contextualized modifications.

    “As the survey items were adopted or adapted with minor contextualized modifications, the CFA was employed to assess the construct validity along with other analyses.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Teaching knowledge forms the basis of students' attitudes toward the behavior and perceived behavioral control, but knowledge of AI alone may not be sufficiently motivating for students to have continuous intention to learn AI.

    “It thus reinforces the previous conclusion that the teaching of knowledge forms the basis of the student's attitudes towards the behavior and perceived behavioral control, but knowledge of AI alone may not be sufficiently motivating for students to have continuous intention to learn AI.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Interpretationreasoning

    The findings may not be generalizable to other students with other AI learning experiences, because the limited sample surveyed went through a similar AI curriculum that shaped their perspectives.

    “The findings may not be generalizable to other students with other AI learning experiences.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The core content of AI curricula does not deviate from the key intent of machine learning to be human-like, with visual and voice recognition and statistical prediction as the main content.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The power of AI is likely to challenge many people and cause anxiety once they know its implications.

    “The power of AI is likely to challenge many and cause anxiety once they know the implications [19].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [19] Wang, Y.Y., and Wang, Y.S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interact. Learn. Environ., 1–16.

      Not checked in the free run.

40 of 40

  • The paper relies on this without support.

    Attending at least one AI course ensures that participants have the relevant knowledge to respond to the survey items.

    “This sampling strategy was adopted so as to ensure that the participants have the relevant knowledge to respond to the items.”

  • The paper relies on this without support.

    Subjective norms has not previously been reported as a factor in the study of students' intention to learn AI.

    “The subjective norms are a new factor in the study of students' intention to learn AI that has not been previously reported.”

  • The paper relies on this without support.

    The core content of AI curricula does not deviate from the key intent of machine learning to be human-like, with visual and voice recognition and statistical prediction as the main content.

    “the core content of AI does not deviate from the key intent of machine learning to be human-like, with visual and voice recognition, statistical prediction, etc. as the main content.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Chai, Wang, and Xu (2020) and Chai, Lin, et al. (2020) designed a 6-item questionnaire to understand students' confidence, perceived relevance of learning AI and readiness towards AI.

    “For example, Chai, Wang, and Xu (2020) and Chai, Lin, et al. (2020) designed a 6-item questionnaire to understand students' confidence, perceived relevance of learning AI and readiness towards AI.”

    From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Chai, Wang, and Xu (2020)

    Section 3.2 (Instruments; page unavailable) describes a student survey that measured confidence in learning AI, perceived usefulness of AI, and intention to learn AI. But it comprised 41 items across nine constructs, not a six-item questionnaire: AI literacy had six items, while confidence had five. The paper does not describe a six-item instrument measuring the three attributes named in the claim. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Learning has been considered as an important part of or addition for AI literacy before.

    “Learning has been considered as an important part of or addition for AI literacy before [8, 10, 13].”

    From MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies (2023) · cites this paper as [13]

    The Introduction calls learning AI an important topic, and §3.2 defines AI literacy in terms of students’ perceived knowledge and skills. But §2.1 treats AI literacy as a background factor for students’ intention to learn AI; it does not explicitly describe learning as a component of, or addition to, AI literacy. The indexed passages provide no page numbers. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Create AI is separate and no part of AI Literacy.

    “This fits in with the general picture that Create AI is separate [13, 23, 29] and no part of AI Literacy.”

    From MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies (2023) · cites this paper as [13]

    The Instruments section (3.2; page unavailable) defines the study’s AI-literacy measure as perceived knowledge and skills in using AI technology, and the Table 1 items concern understanding AI processes rather than creating AI. This supports a distinction in this study’s measure, but the paper does not establish the broader claim that creating AI is categorically separate from, and never part of, AI literacy. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • AI literacy is significantly associated with the aspects including subjective norms, perceived usefulness of AI, AI for social good, attitude toward using AI, AI optimism and confidence in learning AI.

    “It is found that AI literacy is significantly associated with the aspects including subjective norms, perceived usefulness of AI, AI for social good, attitude toward using AI, AI optimism and confidence in learning AI (Chai, Wang, & Xu, 2020;Lin et al., 2021).”

    From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Chai, Wang, & Xu, 2020

  • Most conceptualisations of AI literacy do not explicitly mention the development of AI.

    “This is in line with most conceptualisations of AI literacy where the development of AI is not explicitly mentioned [13, 23, 29]”

    From MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies (2023) · cites this paper as [13]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • An opposite-signed anxiety effect is marked as accepted

    Major

    The paper says anxiety's hypothesized influences are negative, but it marks the positive anxiety-to-behavioral-intention effect as accepted.

  • Conflicting claims about whether AI anxiety predicts confidence

    Major

    The results first say AI anxiety significantly predicts confidence in learning AI, then say it does not. Table 3 lists the anxiety-to-confidence path (H16) as not accepted.

  • Reported minimum factor loading exceeds a loading in Table 1

    Minor

    The stated loading range starts at 0.674, but Table 1 includes a retained item with a loading of 0.650.

  • Attitude-toward-use Cronbach's alpha differs from Table 1

    Minor

    The results report an alpha of 0.82 for attitude toward using AI, whereas Table 1 reports 0.844, which rounds to 0.84.

  • Discussion identifies the boxplots as the model

    Minor

    The discussion says Figure 2 depicts the model, but the Figure 2 caption identifies it as boxplots of factor scores.

5 of 5

Cites

Flagged first, then by citations

Research works printed but not linked: 5

  1. Chai, C.S., Lin, P.Y., Jong, M.S.Y., Dai, Y., Chiu, T.K.F., and Qin, J.J. (2020). Primary school students’ perceptions and behavioral intentions of learning artificial intelligence. Educ. Technol. Soc., in press.
  2. Seldon, A., and Abidoye, O. (2018). The Fourth Education Revolution: Will Artificial Intelligence Liberate or Infantilise Humanity, University of Buckingham Press.
  3. Qin, J.J., Ma, F.G., and Guo, Y.M. (2019). Foundations of Artificial Intelligence for Primary School, Popular Science Press.
  4. Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2010). Multivariate Data Analysis, Prentice Hall. [7th ed.].
  5. Kline, R.B. (2015). Principles and Practice of Structural Equation Modeling, Guilford Press. [4th ed.].

Cited by

Sorted by citations

Related

Authors
Chai, C. S., Wang, X., & Xu, C.
Affiliations
  • Chinese University of Hong Kong (HK)
  • China University of Petroleum, East China (CN)
  • Beijing Normal University (CN)
Corresponding author
  • Wang, Xingwei
  • Xu, Chang
ORCID
Venue
Mathematics
Publisher
MDPI AG
Published
2020-11-23
Volume
8
Issue
11
Pages
2089
ISSN
2227-7390 (ISSN-L)
DOI
10.3390/math8112089
OpenAlex
W3110261028
Cited by
226
References
42
Citations by year
2026: 45 · 2025: 76 · 2024: 53 · 2023: 30 · 2022: 17 · 2021: 4 · 2020: 1
FWCI
12.24
Type
journal article
Language
en
Open access
Licence
Locations
  • Mathematics (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Gender Studies › Gender and Technology in Education
Keywords
artificial intelligence in education, theory of planned behavior, high school students, AI literacy, subjective norms, anxiety, attitudes toward AI, perceived behavioral control, perceived usefulness of AI, AI curriculum design, Chinese students

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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