Improving the Self-esteem Scores of High School Students Through Artificial Intelligence Counseling: A Quantitative, Quasi-experimental Study
Bibliographic record
Abstract
The purpose of this quantitative, quasi-experimental study was to determine the difference in self-esteem scores among high school students who participated in artificial intelligence counseling and those who did not when controlling for pretest scores. Student self-esteem is significant because it influences their work effort, class participation, and social interactions; therefore, research on the effect of artificial intelligence counseling is significant in exploring affordable and effective counseling methods to increase student self-esteem. The researcher used a convenience sample of 74 high school students from a private school in northern Maryland. Participants were evenly distributed into one treatment group and one control group. The study lasted for four weeks during the first quarter of the 2024 academic year, and the Rosenberg Self-Esteem Scale was used as the instrument to measure student self-esteem. Data analysis was conducted using a one-way analysis of covariance. The analysis revealed that there was no significant difference between the scores of high school students who participated in artificial intelligence counseling and those who did not when controlling for pretest scores. The study contributes to the advancement of the field by providing results for a high school population with the use of AI as a tool for counseling and its connection to student self-esteem. Future research should include using a larger sample, increasing the diversity of students used in the study, and extending the duration of the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".