The Effect of Chatbots and AI on The Self-Efficacy, Self-Esteem, Problem-Solving and Critical Thinking of Students
Bibliographic record
Abstract
This article delves into the multifaceted impacts of chatbots and AI in educational settings. It explores how these technologies, increasingly integrated into learning environments, influence key psychological aspects and cognitive skills among students. The review highlights the potential of chatbots in enhancing academic processes, offering personalized learning experiences, and serving as bridges to educational resources. However, it also raises concerns about the ethical use of such technologies. Focusing on psychological aspects, the article reviews literature suggesting that frequent and satisfying interactions with chatbots can enhance students' self-efficacy and engagement. Studies indicate that chatbots might improve self-efficacy in experimental settings and have indirect effects on health-related self-efficacy. In terms of self-esteem and self-confidence, the research presents mixed findings. While chatbots can positively affect body image and self-esteem among certain demographics, over-reliance on these technologies for social interaction or validation might negatively impact real human connections and individual confidence. The article also examines the impact of chatbots on problem-solving skills. Some studies suggest that AI chatbots can enhance problem-solving abilities, especially when integrated into educational systems. However, there is a risk that reliance on chatbots could limit users' exploration of alternative problem-solving strategies. Critical thinking is another area reviewed, with studies presenting diverse results. While some research indicates a positive influence of chatbots on critical thinking, others suggest limitations or context-dependent effects. The article concludes that while AI and chatbots offer transformative potential for enhancing student learning and engagement, their impact is complex and multifaceted. Future advancements in chatbot technology should aim to enhance their positive impact on users' psychological well-being and cognitive development, balancing the need for independent thinking and adaptability to complex problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".