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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue VIII August 2025
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The Innovative Role of Artificial Intelligence (AI) Tools in Enhancing
Academic Buoyancy and Psychological Well-Being among Private
School Students
Wendelyn Mariano
University of Perpetual Help System DALTA – Las Piñas, Philippines
DOI: https://doi.org/10.51244/IJRSI.2025.120800143
Received: 11 Aug 2025; Accepted: 16 Aug 2025; Published: 15 September 2025
ABSTRACT
Artificial Intelligence (AI) has emerged as a transformative force in education, offering adaptive and
personalized learning experiences that can enhance both academic outcomes and student well-being. This
study examined the relationship between AI tool adoption, academic buoyancy, and psychological well-being
among senior high school and college students in a selected private school in Laguna, Philippines. Guided by
the Self-Determination Theory and Cognitive Load Theory, a descriptive-correlational design was employed
using total enumeration sampling. Eighty-two students aged 16–24 completed a validated, paper-based survey
comprising demographic information, the Technology Acceptance Model (perceived usefulness, ease of use,
and user acceptance), the Academic Buoyancy Scale, and the Psychological Well-Being Scale. Results
revealed high levels of AI adoption, academic buoyancy, and psychological well-being across the sample.
Pearson correlation analysis indicated a significant moderate positive relationship between AI adoption and
academic buoyancy (p < 0.05), as well as between AI adoption and psychological well-being (p < 0.05). Age
and educational level significantly influenced academic buoyancy, whereas sex and place of residence did not.
These findings highlight AI tools’ potential to foster resilience, adaptability, and mental well-being when
integrated into supportive educational environments. The study recommends the development of AI-based
programs to promote equitable access, strengthen academic support systems, and enhance student wellness.
Keywords: Artificial Intelligence, Academic Buoyancy, Psychological Well-Being, Technology Acceptance
Model, Self-Determination Theory, Cognitive Load Theory, Private School Students
INTRODUCTION AND BACKGROUND OF THE STUDY
Artificial Intelligence (AI) is transforming multiple sectors, including education, by providing innovative tools
that deliver adaptive, personalized, and data-driven learning experiences. AI refers to the field of computer
science that focuses on creating systems capable of performing tasks that typically require human intelligence,
such as visual recognition, decision-making, and natural language processing (Pellicelli, 2023). In the
educational context, AI is more than a technological add-on; it is reshaping traditional pedagogical models
through intelligent tutoring systems, automated feedback mechanisms, and mental health applications that
address both cognitive and socio-emotional aspects of learning (Wang et al., 2024).
One key area where AI can have a substantial impact is academic buoyancy, defined as students’ capacity to
respond positively to everyday academic challenges, such as managing workloads, meeting deadlines, and
coping with setbacks (Putwain et al., 2020). Studies suggest that academic buoyancy is positively associated
with resilience, motivation, and achievement-related behaviors (Rohinsa et al., 2020; Thomas & Ozer, 2024).
AI’s potential to enhance academic buoyancy lies in its ability to provide personalized learning pathways,
immediate feedback, and tailored resources that strengthen students’ self-regulation and problem-solving skills
(Parra et al., 2024).
Equally important is the role of AI in supporting psychological well-being, a multidimensional construct
encompassing emotional stability, autonomy, positive relationships, and purpose in life (Ryff, 2014). In fast-
paced educational environments, students face increased risks of stress, anxiety, and depression, which can
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue VIII August 2025
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impair academic performance and overall functioning (Alibudbud, 2021). Evidence shows that AI-powered
interventions, such as chatbots, self-help applications, and adaptive cognitive-behavioral tools, can help reduce
anxiety, promote emotional regulation, and enhance life satisfaction (Pavlopoulos et al., 2024; Chan, 2025).
Despite these promising applications, there remain concerns about equitable access to AI tools, data privacy,
and the risk of technostress or overreliance on technology (MDPI, 2024; Villarino, 2024). In the Philippine
context, where private educational institutions serve diverse student populations with varying degrees of digital
literacy, it is crucial to understand how AI adoption relates to both academic resilience and mental health
outcomes.
Grounded in the Self-Determination Theory (SDT) (Deci & Ryan, 1985) and Cognitive Load Theory (CLT)
(Sweller, 1988), this study examines the relationship between AI tool adoption, academic buoyancy, and
psychological well-being among private school students. SDT highlights the importance of autonomy,
competence, and relatedness in fostering motivation and well-being, while CLT underscores the optimization
of mental effort to enhance learning outcomes. Together, these frameworks provide a basis for exploring how
AI tools can simultaneously reduce cognitive overload and satisfy basic psychological needs, ultimately
improving resilience and well-being.
The primary objective of this study is to investigate the extent to which AI adoption predicts academic
buoyancy and psychological well-being in a private school setting. The findings aim to inform educational
policy and practice by guiding the integration of AI technologies into teaching and student support systems in
ways that foster academic success and holistic development.
METHODOLOGY
This study utilized a descriptive-correlational research design to determine the relationship between the
adoption of artificial intelligence (AI) tools, academic buoyancy, and psychological well-being among private
school students. This non-experimental design was selected because it allows the observation of naturally
occurring relationships between variables without manipulation, enabling the measurement of their strength
and direction (Bhandari, 2021; Appinio, 2023). The research was conducted in a PACUCOA-accredited
private school in Laguna, Philippines, which offers both senior high school and college programs. The
institution was considered ideal for the study due to its access to modern digital resources, technology-
enhanced classrooms, and a school health clinic staffed by a licensed nurse, reflecting its commitment to both
academic and student welfare.
The study population consisted of senior high school and college students aged 16–24 years who had prior
experience using AI tools for academic purposes. Inclusion criteria required participants to be officially
enrolled during the June–July 2025 term, possess sufficient comprehension to answer the survey instrument,
and voluntarily consent to participate. Students were excluded if they were absent during data collection, had
no prior AI tool usage, or had conditions that could hinder accurate self-reporting. Given the small size of the
population, total enumeration sampling was employed to include all qualified respondents (Crossman, 2020).
Power analysis using G*Power 3.1.9.2 determined that a minimum of 82 participants was needed to detect a
medium effect size (r = 0.30) with 80% statistical power at a 5% significance level.
Data collection was carried out using a four-part, paper-based, self-administered questionnaire adapted from
established and validated instruments. Part A gathered demographic data, including age, sex, educational level,
and place of residence. Part B was adapted from Davis’ (1989) Technology Acceptance Model (TAM) and
measured perceived usefulness, perceived ease of use, and user acceptance of AI tools (Chandrasekera et al.,
2024). Part C utilized the Academic Buoyancy Scale by Jahedizadeh et al. (2019), which assessed
sustainability, regulatory flexibility, positive personal eligibility, and acceptance of academic life. Part D
measured psychological well-being using Ryff’s (2014) multidimensional model, covering autonomy,
environmental mastery, personal growth, positive relations, purpose in life, and self-acceptance. All items were
rated on a four-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), with adaptations
reviewed for cultural appropriateness and content relevance.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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The instrument underwent rigorous validation and reliability testing before implementation. Content and face
validity were established through the evaluation of 12 experts in education, psychology, and nursing. Items
with a content validity index (CVI) of 0.79 or higher were retained, those between 0.70 and 0.79 were revised,
and items below 0.70 were removed, following the guidelines of Polit and Beck (2021). Reliability testing
through Cronbach’s alpha indicated that all subscales met or exceeded the acceptable threshold of 0.70 (Taber,
2018).
Prior to data collection, permission was obtained from the Dean of the University of Perpetual Help System
Dalta and from the private school’s administration. Eligible students were briefed on the study’s objectives,
procedures, and their rights as participants, and written informed consent was obtained. Surveys were
distributed and completed in classrooms under the researcher’s supervision, taking approximately 10–15
minutes per respondent. Completed questionnaires were collected immediately to ensure completeness and
accuracy.
Data were analyzed using SPSS software. Descriptive statistics such as frequency, percentage, weighted mean,
and standard deviation summarized the demographic characteristics and variable scores. Inferential statistics
included independent samples t-tests to compare academic buoyancy scores by sex and place of residence,
one-way ANOVA to assess differences by age and educational level, and Pearson’s product–moment
correlation to examine relationships between AI adoption, academic buoyancy, and psychological well-being.
The Shapiro–Wilk test assessed normality of continuous variables, and all analyses used a significance level of
α = 0.05.
Ethical principles were strictly observed throughout the research process. The study was approved by the
Institutional Review Board of the University of Perpetual Help System Dalta. Participation was voluntary, and
respondents were assured of confidentiality and anonymity, with the right to withdraw at any time without
consequence. All data were stored securely and accessible only to the researcher, ensuring compliance with the
ethical standards of the Declaration of Helsinki (World Medical Association, 2013).
RESULTS AND DISCUSSION
The analysis of the demographic profile of the 82 respondents revealed that the majority were female, aged
between 16 and 21 years, and residing outside Metro Manila, with most enrolled at the college level. These
demographics provided a diverse but predominantly young adult sample, aligning with the age group most
active in digital technology use and adaptation (Fošner, 2024).
Findings from the Technology Acceptance Model (TAM) showed that students exhibited high levels of AI
adoption, with strong agreement on perceived usefulness, perceived ease of use, and user acceptance. This
suggests that students not only recognized the practical benefits of AI in enhancing their academic tasks but
also found these tools easy to integrate into their learning routines. Such results are consistent with global
trends reporting high acceptance of AI-powered platforms in education, particularly when these tools offer
user-friendly interfaces and demonstrable benefits in academic performance (Parra et al., 2024; Wang et al.,
2024).
Results from the Academic Buoyancy Scale indicated that students scored high across all four dimensions—
sustainability, regulatory flexibility, positive personal eligibility, and acceptance of academic life. These
findings imply that respondents possessed strong coping skills, adaptability, and motivation to persist despite
academic challenges. The positive relationship between AI adoption and academic buoyancy found in this
study mirrors the work of Khasawneh et al. (2024), who noted that AI-driven feedback systems and
personalized learning plans can foster students’ confidence and problem-solving abilities, enabling them to
bounce back from setbacks more effectively.
Similarly, high scores were obtained in the Psychological Well-Being Scale, indicating strong autonomy,
environmental mastery, personal growth, positive relations, purpose in life, and self-acceptance among
participants. The significant positive correlation between AI adoption and psychological well-being supports
the principles of Self-Determination Theory (Deci & Ryan, 1985), which underscores the importance of
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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fulfilling basic psychological needs—autonomy, competence, and relatedness—for optimal mental health. AI
tools that provide tailored learning experiences, mastery-oriented tasks, and opportunities for collaboration can
help meet these needs, leading to improved emotional well-being (Pavlopoulos et al., 2024).
Further analysis revealed that age and educational level significantly influenced academic buoyancy,
suggesting that maturity and advanced academic exposure may enhance resilience and adaptability. These
findings align with previous research indicating that older students often exhibit better self-regulation and
coping strategies (Conway, 2022). On the other hand, no significant differences were found in academic
buoyancy when grouped according to sex or place of residence, indicating that resilience may be influenced
more by individual learning experiences and exposure to academic challenges than by gender or location.
While the results are promising, they should be interpreted with caution. Institutional support, faculty
readiness, and equitable access to AI resources are critical factors that may affect the extent to which AI tools
can influence student outcomes (MDPI, 2024). Furthermore, as Kundu and Bej (2025) warned, overreliance on
AI tools may inadvertently reduce self-initiative and create technostress if not paired with appropriate human
guidance and balanced learning approaches.
This study concludes that AI adoption has a significant positive association with both academic buoyancy and
psychological well-being among private school students. Students who actively and effectively use AI tools
tend to be more adaptable, resilient, and emotionally well-balanced in managing academic demands.
Moreover, demographic variables such as age and educational level influence academic buoyancy, while sex
and place of residence do not appear to play a significant role. These findings underscore the importance of
integrating AI technologies into academic programs not only for cognitive gains but also for promoting mental
wellness and resilience.
Based on the results, several recommendations are proposed. First, schools should integrate AI literacy into
their curricula to maximize the academic and psychological benefits of these technologies while ensuring
ethical and responsible use. Second, AI-supported academic and wellness programs should be developed to
provide personalized learning support, mental health monitoring, and timely interventions for at-risk students.
Third, faculty members should receive targeted training to effectively incorporate AI tools into teaching and
student support services. Lastly, equitable access to AI technologies should be prioritized so that all students,
regardless of socioeconomic background, can benefit from their potential to enhance both learning and well-
being.
This study is limited by its cross-sectional design, which prevents the establishment of causal relationships
between AI adoption, academic buoyancy, and psychological well-being. The use of a single private school as
the research site also limits the generalizability of the findings to other educational contexts. Moreover, data
were based on self-reported measures, which are subject to social desirability bias and recall errors. The study
did not explore the long-term effects of AI adoption, indicating a need for longitudinal research to assess
sustained impacts over time. Future studies may also benefit from incorporating qualitative methods to capture
deeper insights into students’ lived experiences with AI in learning environments.
REFERENCES
1. Alibudbud, R. C. (2021). On the mental health impact of the COVID-19 pandemic among Filipino
students. Asian Journal of Psychiatry, 64, 102768. https://doi.org/10.1016/j.ajp.2021.102768
2. Appinio. (2023). Correlation analysis: Methods and applications.
https://www.appinio.com/en/blog/correlation-analysis
3. Bhandari, P. (2021). An introduction to correlational research. Scribbr.
https://www.scribbr.com/methodology/correlational-research
4. Chandrasekera, T., Yoon, S., & Lee, H. (2024). Extending the technology acceptance model in
education: AI-assisted learning tools. Computers & Education, 196, 104741.
https://doi.org/10.1016/j.compedu.2024.104741
5. Chan, C. (2025). Artificial intelligence in mental health support: Opportunities and challenges. Journal
of Educational Technology Research, 38(2), 155–172.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue VIII August 2025
Page 1638
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6. Conway, C. (2022). Academic buoyancy as a predictor of academic stress among college students.
Philippine Journal of Psychology, 55(2), 87–102.
7. Crossman, A. (2020). Understanding purposive sampling. ThoughtCo.
https://www.thoughtco.com/purposive-sampling-3026727
8. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information
technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
9. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior.
Springer.
10. Fošner, A. (2024). Student perceptions of AI in higher education: Opportunities and challenges.
International Journal of Educational Technology, 21(1), 34–49.
11. Jahedizadeh, S., Ghonsooly, B., & Ghanizadeh, A. (2019). Academic buoyancy: A scale validation in
the EFL context. The Asia-Pacific Education Researcher, 28(1), 65–75. https://doi.org/10.1007/s40299-
018-0407-2
12. Khasawneh, O., Gosling, C., & Williams, B. (2024). AI-assisted learning and student resilience: A
systematic review. Computers & Education, 198, 104792.
https://doi.org/10.1016/j.compedu.2024.104792
13. Kundu, A., & Bej, T. (2025). Balancing AI integration in education: Avoiding overdependence and
technostress. Educational Technology Research and Development, 73(2), 211–230.
14. MDPI. (2024). Artificial intelligence in education: Implications for teaching and learning.
Sustainability, 16(4), 2145. https://doi.org/10.3390/su16042145
15. Parra, A., González, M., & Ruiz, P. (2024). Adaptive AI in higher education: A systematic review.
Computers & Education, 198, 104789. https://doi.org/10.1016/j.compedu.2024.104789
16. Pavlopoulos, V., Papadopoulou, D., & Karampelas, V. (2024). Artificial intelligence applications in
promoting mental health. Frontiers in Psychology, 15, 1278932.
https://doi.org/10.3389/fpsyg.2024.1278932
17. Pellicelli, M. (2023). Artificial intelligence and the future of education. Journal of Educational
Management, 35(2), 115–130.
18. Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing
practice (11th ed.). Wolters Kluwer.
19. Putwain, D., Daly, A., Chamberlain, S., & Sadreddini, S. (2020). Academically buoyant students are
less anxious about tests and perform better in examinations. British Journal of Educational Psychology,
90(2), 272–287. https://doi.org/10.1111/bjep.12283
20. Rohinsa, A., Wibowo, S., & Nugroho, H. (2020). Academic buoyancy as a mediator in the relationship
between parental support and academic performance. International Journal of Instruction, 13(4), 625–
642. https://doi.org/10.29333/iji.2020.13439a
21. Ryff, C. D. (2014). Psychological well-being revisited: Advances in the science and practice of
eudaimonia. Psychotherapy and Psychosomatics, 83(1), 10–28. https://doi.org/10.1159/000353263
22. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science,
12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
23. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments
in science education. Research in Science Education, 48(6), 1273–1296.
https://doi.org/10.1007/s11165-016-9602-2
24. Thomas, M., & Ozer, M. (2024). The link between academic buoyancy and test anxiety in high school
students. School Psychology International, 45(1), 58–74. https://doi.org/10.1177/01430343231175327
25. Villarino, A. (2024). Rural perspectives on AI in Philippine higher education: Adoption, barriers, and
opportunities. Philippine Journal of Educational Technology, 12(1), 45–62.
26. Wang, X., Liu, H., & Zhang, Y. (2024). AI in personalized learning: A meta-analysis. Educational
Technology & Society, 27(1), 25–40.
27. World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical
principles for medical research involving human subjects. JAMA, 310(20), 2191–2194.
https://doi.org/10.1001/jama.2013.281053
