The DOI matches its Crossref record.
AI Quality Cultivation and Application Ability Training for Normal University Students
Abstract
The rapid integration of AI technology and education will have a profound impact on the traditional education and teaching ideas, forms, contents, methods, and will also put forward new and higher requirements for teachers' professional quality and teaching ability. For normal university students, AI quality cultivation and application ability training are particularly important. Based on the previous research, this paper holds that digital literacy, computational thinking, programming ability and deep cognition of intelligent society are the important contents of AI technology literacy of normal university students. And put forward that under the AI environment, normal university students' learning ability, teaching ability, teaching research ability three aspects, as well as basic ability, deepening application, application innovation three levels of ability training strategy. And points out that normal university students should learn AI technology and theory in practice, cultivate AI quality and application ability in teaching, and improve human-machine cooperation skills in exploration.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“For example, some authors postulated that programming skills are one of the main AI literacies (e.g., Liu & Xie, 2021), while others focused more on reflective and evaluative skills (e.g., Ng et al., 2021a).”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Liu & Xie, 2021
In the citing paper
“In East Asia, research is mainly dominated by China (Kong et al., 2021;Liu & Xie, 2021;Ng et al., 2021aNg et al., , 2021b) ) and other technophile states like Taiwan (Lin et al., 2021;Shih et al., 2021).”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Liu & Xie, 2021
In the citing paper
“Eight studies (27%) specified particular skills or competency categories which are at the core of AI literacy (Cetindamar et al., 2022;Kong et al., 2021;Liu & Xie, 2021;Ng et al., 2021a;Ng et al., 2021b;Park & Suh, 2021;Register & Ko, 2020;Wiljer & Hakim, 2019)”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Liu & Xie, 2021
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 8
- 0, ISTE Teacher Standards [dB / OL] [2008-12-10]
- kaiquan, 2017, Technological Path and Practical Exploration of Artificial Intelligence 2.0 Reshaping Learning [J], Journal of Distance Education
- wenqing, 2019, The Age of Artificial Intelligence: How to Adapt Curriculum Teaching to Changes, China Education Daily
- wenshuang, 2017, Standard Framework of Teachers' Teaching Ability in the Digital Age, Modern Distance Education Research
- shengquan, 2019, The Future Role of AI Teachers, China Teachers' Daily
- xiaoya, 2019, Connotation Analysis and Promotion Strategies of Teachers' Information Literacy from the Perspective of Artificial Intelligence, Chinese Journal of Education
- 0, ICT Competency Teacher Framework [dB / OL]
- kai, 2019, Reconstructing Educational Ecology is the New Mission of Artificial Intelligence, China Education Daily
Cited by
Sorted by citations
Artificial intelligence literacy in higher and adult education: A scoping literature review
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658
Enhancing artificial intelligence literacy through cross-cultural online workshops
Korte, S.-M., Cheung, W. M.-Y., Maasilta, M., Kong, S.-C., Keskitalo, P., Wang, L., Lau, C. M., Lee, J. C. K., & Gu, M. M. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100164 · Cited by 49
Metadata
- Authors
- Liu, S., & Xie, X.
- Affiliations
- Guizhou Normal University (CN)
- Venue
- 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)
- Publisher
- IEEE
- Published
- 2021-07
- Pages
- 116-120
- OpenAlex
- W3217333539
- Cited by
- 15
- References
- 0
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5 · 2023: 1 · 2022: 1
- FWCI
- 1.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Network and Information Systems for Computers (ICNISC) (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Digital literacy in education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- AI literacy, normal university students, teacher professional development, digital literacy, computational thinking, programming skills, teaching ability, learning ability, human-machine collaboration
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: AI Quality Cultivation and Application Ability Training for Normal University Students. https://app.cochat.ai/papers/10.1109/icnisc54316.2021.00030
- The paper
- Liu, S., & Xie, X. (2021). AI Quality Cultivation and Application Ability Training for Normal University Students. 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC). https://doi.org/10.1109/icnisc54316.2021.00030
