Assessment of the Impact of Artificial Intelligence on College Student Learning Based on the CRITIC Method
Abstract
This paper investigates the impact of artificial intelligence (AI) on college students' learning. It employs a questionnaire survey to collect data and establishes an evaluation index system. The survey data is visualized through charts and analyzed for correlations with students' characteristics and responses. Single-choice questions are quantified using positive values, while multiple-choice questions use a numerical accumulation approach. The survey questionnaire is divided into four key indicators: autonomous learning ability, learning motivation, learning efficiency improvement, and learning progress. These indicators are rigorously analyzed for correlation and are found to be effectively uncorrelated, forming a robust evaluation index system. The CRITIC analysis method assigns objective weights to these indicators (0.2118, 0.1924, 0.3540, and 0.2419, respectively) for each survey sample. A group decision-making model is applied to explore AI's impact on students' learning. This study offers valuable insights into the influence of AI on college students' learning experiences, providing a foundation for educational decision-making to enhance the overall education system and improve students' learning outcomes.
Integrity signals
Registry
Cites
Statistics
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?
Overdependence on AI may weaken students' conceptual depth and mastery of foundational learning skills.
“However, Huang et al. [61] caution that overdependence on AI may weaken students' conceptual depth and mastery of foundational learning skills.”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [61]
Cites
Discriminative feature extraction from X-ray images using deep convolutional neural networks
Srinivas, M., Roy, D., & Mohan, C. K. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7471809 · Cited by 62 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Adamakis, M., & Rachiotis, T. (2025) · Encyclopedia · doi:10.3390/encyclopedia5040180 · Cited by 76 · see citing works
- Authors
- Huang, X., Zhang, Y., & Lyu, X.
- Affiliations
- Qinghai Minzu University,School of Economics and Management,Xining,China
- Qinghai Minzu University,School of Civil and Transportation Engineering,Xining,China,Qinghai
- Qinghai Minzu University,School of Mathematics and Statistics,Xining,China,Qinghai
- ORCID
- Zhang, Yiwei
- Venue
- 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT)
- Publisher
- IEEE
- Published
- 2023-10-20
- Pages
- 1-12
- OpenAlex
- W4391094414
- Cited by
- 1
- References
- 1
- Citations by year
- 2025: 1
- FWCI
- 0.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- artificial intelligence in education, university students, learning outcomes, autonomous learning ability, student motivation, learning efficiency, learning progress, evaluation index system, CRITIC weighting, group decision making
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
