The Effect of Chatbots and AI on The Self-Efficacy, Self-Esteem, Problem-Solving and Critical Thinking of Students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it