Pith. sign in

REVIEW 4 major objections 4 minor 22 references

AISCliteracy: Assessing Artificial Intelligence and Cybersecurity Literacy Levels and Learning Needs of Students

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A survey of 303 Nepalese secondary students finds moderate-to-high AI literacy, high cybersecurity awareness, and little formal training in either area.

desk verdict A useful new dataset on Nepalese students' self-reported AI/cybersecurity awareness, but the paper overstates the connection between what students say and what they actually know. read the letter →

arxiv 2506.23321 v1 pith:G5HQLEBC submitted 2025-06-29 cs.CY cs.CR

Devendra Chapagain , Naresh Kshetri , Bishwo Prakash Pokharel This is my paper · ORCID
classification cs.CYcs.CR
keywords AIeducationliteracyCybersecuritySecondaryNepalLearningneedsSurveyresearchRelativeImportanceIndex
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

The paper sets out to establish that secondary students in Nepal's Chitwan District already have moderate-to-high AI literacy and high awareness of cybersecurity threats, while formal education lags behind: schools rarely provide AI resources or hands-on cybersecurity training. This matters because Nepal's national curriculum has not yet integrated AI and cybersecurity topics, and the survey offers local evidence about where to start. The picture comes from 303 self-report questionnaires completed by students in grades 9 to 12, ranked through a Relative Importance Index rather than an objective test.

What carries the argument

The central object is the Relative Importance Index (RII), computed as the weighted average of five-point Likert responses and used to rank 19 statements about AI and cybersecurity. It is the mechanism that converts self-reported agreement into literacy levels, allowing the paper to identify where students score high, such as threat awareness, and where the system scores low, such as school resources and training workshops.

What would settle it

A short objective multiple-choice test on AI concepts and cyber hygiene given to the same 303 students, compared item-by-item with their self-reported RII scores, would settle whether the reported literacy levels reflect knowledge or confidence.

Watch

Extended reading notes

Core claim

The central claim, stated in the discussion section, is that AI literacy levels are moderate to high, with students showing a strong foundational understanding of AI concepts and a positive outlook on AI's role in their future, while cybersecurity awareness is high but practical training is minimal. The data show students recognize everyday AI uses and know common threats such as phishing and malware, but the lowest-scoring items are school-provided AI resources and completed cybersecurity workshops. The paper reads this as evidence that schools, not students, are the weak link, and that curriculum expansion in advanced AI topics and hands-on cybersecurity practice should follow.

Load-bearing premise

The argument depends on treating students' agreement with statements like 'I have a good understanding of what AI is' as a valid measure of AI literacy, without any check against objective knowledge.

Editorial extensions

If this is right

  • Curriculum planners in Chitwan have evidence that students are receptive: interest in learning more about AI had one of the highest RII values in the survey.
  • School-based AI courses, especially on machine learning and robotics, would target the largest knowledge gap the survey identifies.
  • Cybersecurity education should shift from awareness toward hands-on practice, because completed training workshops scored among the lowest items.
  • Formal schooling, rather than home internet access, is presented as the main lever for raising AI literacy, so investment in school resources should take priority.

Reading between the lines

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

  • The reliance on self-reported agreement means the measured 'literacy' may be confidence rather than competence, so an objective knowledge test is a natural next check.
  • The claim that home internet access makes no significant difference is stated without a reported statistical test, so the conclusion that formal schooling is decisive is stronger than the currently shown evidence.
  • The same instrument could be extended to other districts and to lower grades, where the paper expects the gaps to be larger, to test whether school resources remain the main lever.
  • The low scores on formal training suggest that simply adding cybersecurity content to the curriculum may be less effective than requiring practice-based workshops, an assumption the survey itself cannot test.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper reports a questionnaire survey of 303 secondary students in Chitwan District, Nepal, measuring self-reported AI and cybersecurity literacy via Likert items, summarized with Relative Importance Index (RII) values. The authors conclude that students have moderate-to-high AI literacy, high cybersecurity awareness but minimal practical training, and that schools are the weak link in providing formal education. They also claim no significant difference in AI literacy by home internet access and offer policy recommendations.

Significance. If the measurements were validated, the study would provide a rare empirical baseline for AI and cybersecurity education in a lower-resource context, and its policy recommendations would be actionable. The paper is honest about its self-report limitation in Section 8, and the RII computations are transparent descriptive statistics. However, the central literacy claims are not supported with instrument validation or inferential statistics, so the contribution is currently a descriptive confidence survey rather than a robust literacy assessment.

major comments (4)
  1. [Section 7.1 / Table 1] The claim that students have 'moderate to high' AI literacy rests on RII values of self-reported Likert agreement, but no evidence is presented that these items measure actual knowledge: there is no validation against an objective test, no reliability analysis (e.g., Cronbach's alpha), and no discussion of social desirability or acquiescence bias. Since Section 8 concedes that the study 'relies on self-reported data,' the headline literacy claim should be re-framed as perceived understanding or confidence, or supplemented with validation evidence.
  2. [Section 7.3] The statement that internet access makes 'no significant difference' to AI literacy is unsupported because no statistical test (e.g., t-test, Mann-Whitney, chi-square) or effect size is reported. Similarly, the claim that school resources are 'associated with higher levels of understanding' is asserted without any correlation or regression analysis. Please either report the relevant tests or remove/rephrase these claims.
  3. [Table 1] The 'RANKS' column is internally inconsistent with the RII values: for instance, item 6 ('I am interested in learning more about AI') has the highest AI RII (0.83696) but is ranked 5, while item 5 (RII 0.72277) is ranked 1. Because Figure 2 plots these ranks and Section 7 discusses 'highest-ranked' statements, the ranking must be corrected or its derivation explained.
  4. [Section 6] The methodology states a mixed-methods design with qualitative interviews, but no interview data or analysis appear anywhere in the results; please either report the qualitative component or remove the claim.
minor comments (4)
  1. [Section 7.4] In the paragraph beginning 'On the other hand,' the phrase 'resources of courses' should be 'resources or courses'.
  2. [Section 5.2] 'This initiative-taking approach' is a non-standard phrase; it should likely read 'This proactive approach.'
  3. [Section 7] The reported percentages for internet access do not sum to 100% (97.69% + 1.98% = 99.67%); please clarify the missing remainder or correct the values.
  4. [Abstract] The abstract claims the paper concludes with discussions of affordances and barriers for 'students from lower classes,' but Sections 7 and 8 do not discuss lower classes.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: RII values are direct aggregates of survey data; ‘moderate to high literacy’ is an untested validity inference, not a construction.

full rationale

The paper's empirical claim is a descriptive summary of survey responses. The RII is explicitly defined as the weighted average of 5-point Likert responses (Section 6: “The RII was calculated as the weighted average of responses, with higher RII values indicating greater importance”), so Table 1 values are arithmetic aggregates of raw reported agreement, not fitted constants or quantities defined in terms of the conclusions. Statements such as “I have a good understanding of what AI is” (RII 0.78020) are used to infer literacy, but this is an instrument-validity assumption, not circularity: the inference could be wrong, but it does not make the conclusion true by construction. The paper's other load-bearing claims, such as schools offering limited AI/cybersecurity resources and students lacking formal cybersecurity training, are direct readings of low RII values like 0.65215, 0.75050, and 0.45149. There is no parameter fitted to a subset and then “predicted”; no uniqueness theorem or previous result by these authors is invoked to exclude alternatives; and the acknowledged limitation in Section 8 (“limited by its scope and reliance on self-reported data”) confirms that the data-to-conclusion chain is empirical rather than tautological. The Section 7.3 statement that internet access makes “no significant difference” lacks a reported statistical test, but absent evidence is a correctness or rigor concern, not circularity. Because the derivation is self-contained on the circularity axis, the score is 0.

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

The analysis adds no free parameters and no invented entities. Its conclusions rest on assumptions about self-report validity, sample representativeness, RII cutoff interpretation, and Likert interval scaling, none of which are independently established in the paper.

assumptions (4)
  • domain assumption Self-reported Likert agreement is a valid proxy for actual AI and cybersecurity literacy.
    The paper's central literacy conclusions in Sections 7.1 and 7.2 are based entirely on RII values of self-ratings such as 'I have a good understanding of what AI is' (Table 1), with no objective test or instrument validation.
  • domain assumption The 303 students sampled with stratified random sampling represent the secondary student population of Chitwan District.
    Section 6 describes the sampling design but gives no strata, school-selection criteria, response rate, or comparison with the district population, so representativeness is assumed.
  • domain assumption RII thresholds around 0.72 to 0.84 can be interpreted as 'moderate to high' literacy.
    The paper maps RII values to qualitative literacy levels without citing a validated cutoff or norm.
  • domain assumption A five-point Likert scale is treated as an interval scale for RII computation.
    RII is computed as a weighted average of Likert scores, which assumes equal spacing between response categories.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AISCliteracy: Assessing Artificial Intelligence and Cybersecurity Literacy Levels and Learning Needs of Students." pith.science (2026). https://pith.science/paper/G5HQLEBC

@misc{pith2026250623321,
  author       = {Pith},
  title        = {Pith review of: AISCliteracy: Assessing Artificial Intelligence and Cybersecurity Literacy Levels and Learning Needs of Students},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G5HQLEBC}},
  note         = {Machine review of arXiv:2506.23321}
}
read the original abstract

Artificial intelligence (AI) is rapidly transforming global industries and societies, making AI literacy an indispensable skill for future generations. While AI integration in education is still emerging in Nepal, this study focuses on assessing the current AI literacy levels and identifying learning needs among students in Chitwan District of Nepal. By measuring students' understanding of AI and pinpointing areas for improvement, this research aims to provide actionable recommendations for educational stakeholders. Given the pivotal role of young learners in navigating a rapidly evolving technological landscape, fostering AI literacy is paramount. This study seeks to understand the current state of AI literacy in Chitwan District by analyzing students' knowledge, skills, and attitudes towards AI. The results will contribute to developing robust AI education programs for Nepalese schools. This paper offers a contemporary perspective on AI's role in Nepalese secondary education, emphasizing the latest AI tools and technologies. Moreover, the study illuminates the potential revolutionary impact of technological innovations on educational leadership and student outcomes. A survey was conducted to conceptualize the newly emerging concept of AI and cybersecurity among students of Chitwan district from different schools and colleges to find the literacy rate. The participants in the survey were students between grade 9 to 12. We conclude with discussions of the affordances and barriers to bringing AI and cybersecurity education to students from lower classes.

Figures

Figures reproduced from arXiv: 2506.23321 by the authors.

Figure 2
Figure 2. Ranks vs. AI knowledge questions plot In terms of awareness, students demonstrated a clear understanding of the benefits and risks associated with AI, as indicated by the relatively high RII of 0.82970 for “I am aware of the potential benefits and risks of AI”. This awareness is further reflected in the positive outlook students have on AI, with an RII of 0.72277 for “I believe AI will have a positive impact on my f… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 17 canonical work pages

  1. [1]

    AI technologies are increasingly being integrated into diverse fields, including healthcare, finance, education, and transportation

    Introduction: Artificial intelligence (AI) is rapidly transforming our world, impacting various aspects of our lives from daily routines to complex decision-making processes. AI technologies are increasingly being integrated into diverse fields, including healthcare, finance, education, and transportation. As AI continues to evolve, it becomes critical fo...

    work page 2025

  2. [2]

    Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

    Lameras P, Arnab S. Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education. Information. 2022; 13(1):14. https://doi.org/10.3390/info13010014

    work page doi:10.3390/info13010014 2022

  3. [3]

    Artificial Intelligence in Education: A Review,

    L. Chen, P. Chen, and Z. Lin, "Artificial Intelligence in Education: A Review," in IEEE Access, vol. 8, pp. 75264-75278, 2020, doi: 10.1109/ACCESS.2020.2988510

    arXiv 2020

  4. [4]

    (2021, March)

    Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021, March). Developing middle school students' AI literacy. In Proceedings of the 52nd ACM technical symposium on computer science education (pp. 191-197)

    work page 2021

  5. [5]

    National Education Policy 2076

    Educational policies, AI, and Cybersecurity initiatives in Nepal As Nepal navigates the challenges and opportunities presented by the digital age, integrating artificial intelligence (AI) and cybersecurity into its educational framework has become increasingly important. However, the country’s progress in these areas has been gradual, reflecting broader s...

    work page 2016

  6. [6]

    Xieling Chen, Haoran Xie, Di Zou, Gwo-Jen Hwang, Application and theory gaps during the rise of Artificial Intelligence in Education, Computers and Education: Artificial Intelligence, Volume 1, 2020, 100002, ISSN 2666-920X, https://doi.org/10.1016/j.caeai.2020.100002

    arXiv 2020

  7. [7]

    U., & Imran, M

    Iqbal, M., Khan, N. U., & Imran, M. (2024). The role of Artificial Intelligence (AI) in transforming educational practices: Opportunities, challenges, and implications. Qlantic Journal of Social Sciences, 5(2), 348–359. https://doi.org/10.55737/qjss.349319430

    work page doi:10.55737/qjss.349319430 2024

  8. [8]

    Tuomi, I. (2023). Beyond mastery: Toward a broader understanding of AI in Education. International Journal of Artificial Intelligence in Education, 34(1), 20–30. https://doi.org/10.1007/s40593-023-00343-4

    work page doi:10.1007/s40593-023-00343-4 2023

Show all 22 references
  1. [9]

    Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236

    2020

  2. [10]

    Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. doi:10.1016/j.caeai.2021.100020

    2021

  3. [11]

    Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two Decades of Artificial Intelligence in Education: Contributors, Collaborations, Research Topics, Challenges, and Future Directions. Educational Technology & Society, 25(1), 28–47. https://www.jstor.org/stable/48647028

    2022

  4. [12]

    Yi, Y. (2021). Establishing the concept of AI literacy: Focusing on competence and purpose. JAHR-–European Journal of Bioethics, 12 (2), 353–368

    2021

  5. [13]

    Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3)

    2021

  6. [14]

    Maqsood, S., & Chiasson, S. (2021). Design, development, and evaluation of a cybersecurity, privacy, and digital literacy game for tweens. ACM Transactions on Privacy and Security (TOPS), 24(4), 1-37

    2021

  7. [15]

    Rahman, N. A. A., Sairi, I. H., Zizi, N. A. M., & Khalid, F. (2020). The importance of cybersecurity education in school. International Journal of Information and Education Technology, 10(5), 378-382

    2020

  8. [16]

    T., Pileggi, S

    Alshahrani, B. T., Pileggi, S. F., & Karimi, F. (2024). A Social Perspective on AI in the Higher Education System: A Semi systematic Literature Review. Electronics, 13(8),

    2024

  9. [17]

    Uygun, D. (2024). Teachers’ perspectives on artificial intelligence in education. Advances in Mobile Learning Educational Research, 4(1), 931-939. https://doi.org/10.25082/AMLER.2024.01.005 Page 11 of 11 [ 17] Karki, H. (2019). A brief history of public education, information ...

    2024 doi

  10. [18]

    Government of Nepal. (2019). National Education Policy

    2019

  11. [19]

    (n.d.-b)

    Nepal ICT in education master plan, 2013-2017 | ICT in education ... (n.d.-b). https://www.ictedupolicy.org/resource-library/content/nepal-ict-education-master-plan-2013-2017

    2013

  12. [20]

    Joshi, D. R. (2017). Policies, practices and barriers of ICT utilization in school education in Nepal. International Journal of Research in Social Sciences, 7(2), 408-417

    2017

  13. [21]

    (n.d.-a)

    Cybersecurity Awareness Assessment Report for Nepal. (n.d.-a). https://nta.gov.np/uploads/contents/Cybersecurity-Awareness-Report-2015.pdf

    2015

  14. [22]

    Zeadally, S., Adi, E., Baig, Z., & Khan, I. A. (2020). Harnessing artificial intelligence capabilities to improve cybersecurity. Ieee Access, 8, 23817-23837

    2020

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.