Categoría: Education, Teaching, Learning and Assessment
REVIEW
Impact of Artificial Intelligence on learning behaviors and psychological well-being of college students
Impacto de la inteligencia artificial en los comportamientos de aprendizaje y el bienestar psicológico de los estudiantes universitarios
Diana Catalina Velastegui-Hernandez1
*, Mayra Lucia
Rodriguez-Pérez1 *, Luis Fabián
Salazar-Garcés1 *
1 Faculty of Health Sciences. Technical University of Ambato. Ambato, Ecuador.
Cite as: Velastegui-Hernandez DC, Salazar-Garcés LF, Rodriguez-Pérez ML. Impact of Artificial Intelligence on learning behaviors and psychological well-being of college students. Salud, Ciencia y Tecnología - Serie de Conferencias. 2023 2:582. https://doi.org/10.56294/sctconf2023582
Received: 16-06-2023 Revised: 17-09-2023 Accepted: 22-12-2023 Published: 23-12-2023
Editor: Dr.
William Castillo-González
ABSTRACT
Introduction: the integration of artificial intelligence (AI) systems in education has sparked debate regarding their impact on the psychological well-being of university students. As mental health is crucial for their development and academic success, it is essential to assess how interactions with technology affect their psyche.
Objective: this article aims to provide a systematic review of studies investigating the impact of AI on the psychological well-being of university students, identifying trends, effects, and areas requiring further research.
Method: a comprehensive search was conducted in databases such as PubMed, Scopus, Web of Science, and PsycINFO, using terms related to AI and mental health. Empirical studies published between 2015 and 2023 were included. The selection and analysis of studies were guided by PRISMA guidelines.
Discussion: the review indicates that while some AI systems offer personalized support benefiting learning and mental health, others may generate stress and anxiety due to information overload and a lack of meaningful human interaction. Underlying psychological theories explaining these phenomena are discussed.
Conclusions: educational technology designers must integrate psychological principles in the development of AI tools to maximize benefits and minimize risks to student well-being. Future research should explore in depth how specific features of AI affect different dimensions of psychological well-being.
Key words: Artificial Intelligence in Education; Psychological Well-being; Student Mental Health; Educational Technologies; Psychosocial Impact.
RESUMEN
Introducción: la integración de sistemas de inteligencia artificial (IA) en la educación ha suscitado debate sobre su impacto en el bienestar psicológico de estudiantes universitarios. Dado que la salud mental de esta población es fundamental para su desarrollo y éxito académico, es crucial evaluar cómo las interacciones con la tecnología afectan su psiquis.
Objetivo: este artículo busca proporcionar una revisión sistemática de los estudios que investigan el impacto de la IA en el bienestar psicológico de los estudiantes universitarios, identificando tendencias, efectos y áreas que requieren mayor investigación.
Método: se realizó una búsqueda en bases de datos como PubMed, Scopus, Web of Science y PsycINFO, usando términos relacionados con la IA y la salud mental. Se incluyeron estudios empíricos publicados entre2015 y 2023. La selección y análisis de estudios se guiaron por las directrices PRISMA.
Discusión: la revisión indica que algunos sistemas de IA ofrecen apoyo personalizado que beneficia el aprendizaje y la salud mental, mientras otros pueden generar estrés y ansiedad por la sobrecarga de información y la falta de interacción humana significativa. Se discuten las teorías psicológicas subyacentes que explican estos fenómenos.
Conclusiones: los diseñadores de tecnología educativa deben integrar principios psicológicos en el desarrollo de herramientas de IA para maximizar los beneficios y minimizar los riesgos para el bienestar estudiantil. Futuras investigaciones deben explorar cómo las características específicas de la IA afectan las distintas dimensiones del bienestar psicológico.
Palabras clave: Inteligencia Artificial en Educación; Bienestar Psicológico; Salud Mental de Estudiantes; Tecnologías Educativas; Impacto Psicosocial.
INTRODUCTION
The integration of Artificial Intelligence (AI) into educational settings has revolutionized learning methodologies and the psychological landscape of university students.(1) As educational institutions increasingly deploy AI tools, from personalized learning algorithms to automated administrative support, understanding their impacts on student behavior and well-being has become crucial.(2,3) This literature review aims to consolidate existing research on the dual influence of AI on learning behaviors and the psychological well-being of college students, offering a comprehensive overview of both beneficial outcomes and potential challenges.
AI technologies, designed to adapt and respond to the needs of individual learners, promise to transform traditional educational paradigms.(4) Systems such as adaptive learning platforms can tailor content to the learner’s pace and understanding, potentially enhancing learning efficiency and engagement.(5,6) Moreover, AI-driven analytics help educators identify and address individual learning challenges, potentially reducing barriers to academic success.(7,8) However, the implications of these technologies extend beyond academic performance, influencing various dimensions of students’ psychological well-being.(9,10)
Psychological well-being in the context of AI-enhanced education includes aspects such as stress levels, anxiety, and overall mental health.(2) Preliminary studies suggest that while AI can provide significant support, its improper integration can also lead to increased stress and dependency on technology, potentially hindering emotional and cognitive development.(2) For instance, the constant monitoring and data collection inherent in some AI applications can evoke concerns about privacy and autonomy, contributing to anxiety among students.(2,11)
Furthermore, the disparity in access to advanced technological tools can exacerbate existing educational inequalities, impacting students from lower socioeconomic backgrounds disproportionately.(12,13) Such disparities highlight the need for policies that ensure equitable access to AI resources, ensuring that the benefits of AI do not disproportionately favor the already advantaged groups.
The effectiveness of AI in education also depends on its alignment with pedagogical goals and the psychological makeup of the student body.(3,14) It is essential to consider not only the cognitive but also the emotional and social implications of AI applications in educational environments.(14) For instance, while AI can facilitate personalized learning, it may also reduce face-to-face interactions, potentially impacting students’ social skills and emotional intelligence.(3,15)
This review will systematically examine empirical evidence from various studies, exploring how AI affects learning behaviors such as engagement, retention, and academic performance, alongside its broader impacts on psychological well-being, including stress, anxiety, and self-efficacy. By synthesizing findings from diverse contexts and methodologies, this paper aims to provide a balanced view of the opportunities and challenges posed by AI in higher education.
In conclusion, as AI becomes more embedded in educational systems, it is imperative to adopt a multidisciplinary approach to understand its full impact. This review seeks to contribute to this understanding by offering insights into how AI technologies influence not only the academic but also the psychological dimensions of university students’ lives.
Definition of Key Terms
To ensure clarity and precision in discussing the complex interactions between artificial intelligence (AI), learning behaviors, and psychological well-being, it is imperative to define the key terms that will be frequently referenced throughout this review. The terminologies adopted not only anchor the discussion but also align with the scholarly understanding pertinent to educational technologies. As AI continues to permeate educational settings, it reshapes traditional learning landscapes and necessitates a reassessment of how these technologies influence student engagement and mental health. By explicating terms such as “Artificial Intelligence,” “Learning Behaviors,” and “Psychological Well-being,” this section aims to provide a foundational framework that supports a nuanced analysis of the empirical findings discussed later in this paper. The definitions provided will reflect both the technological aspects of AI in educational systems and their psychosocial impacts, ensuring a holistic view of the subject matter.
Artificial Intelligence (AI): in the context of this review, AI refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.(16,17) AI in education typically encompasses technologies such as machine learning algorithms, natural language processing, and robotics, which are used to enhance educational processes and personalization.(18,19)
Learning Behaviors: Learning behaviors are the observable actions or processes through which students engage with educational content and contexts to acquire or modify knowledge, skills, attitudes, or behaviors.(20) In the framework of AI-enhanced education, these behaviors extend to interactions with AI tools, adaptation to personalized learning environments, and engagement with automated content delivery systems.(21,22)
Psychological Well-being: Psychological well-being in this review is defined as a state encompassing the presence of positive emotions and moods (e.g., contentment, happiness), the absence of negative emotions (e.g., depression, anxiety), satisfaction with life, fulfillment and positive functioning.(23) In educational settings, this includes students’ emotional and mental health states as they interact with both traditional and AI-enhanced learning environments.(10,24)
Adaptive Learning Platforms: These are AI-driven platforms that adjust the educational content according to the unique needs of each learner.(25) By analyzing data on student performance and learning habits, these systems provide personalized resources and learning paths, aiming to optimize the learning experience for effectiveness and efficiency.(5)
Automated Administrative Support: Refers to the use of AI technologies to automate routine tasks traditionally performed by human administrators in educational institutions, such as scheduling, student inquiries, and management of educational resources. This support is intended to streamline operations and allow educational staff to focus more on teaching and less on administrative duties.(5,26)
Natural Language Processing (NLP): A branch of AI that deals with the interaction between computers and humans through the natural language. In educational settings, NLP is used to develop tools that can understand and respond to student inputs in their natural language, facilitating more intuitive learning and interaction.(28,29)
Machine Learning Algorithms: In the educational context, these are algorithms that learn from and make predictions or decisions based on data.(29) Machine learning is used in various educational applications, from predictive analytics identifying at-risk students to adaptive learning systems that customize educational content.(30,31)
AI Tools in Education
In the evolving landscape of higher education, a variety of Artificial Intelligence (AI) technologies have been integrated into university environments, significantly enhancing both the teaching methodologies and the administrative frameworks.(18,19) These AI tools are not just supplementary elements but are becoming central to educational delivery and student engagement strategies.
Learning Management Systems (LMS): Modern LMSs are increasingly powered by AI to provide more personalized learning experiences and to automate administrative tasks.(32,33) AI in LMS can track student progress, predict learning outcomes, and tailor content to meet the individual needs of students.(32) For instance, systems like Canvas and Moodle now incorporate AI to analyze student discussion inputs and provide automated feedback, helping to streamline the learning process and identify students who might require additional support.(34,35,36)
Intelligent Tutoring Systems (ITS): These systems represent a significant leap in personalized education, using AI to simulate one-on-one tutoring experiences.(37,38) ITS utilize complex algorithms to assess student knowledge levels and adapt instructional strategies accordingly.(37) They provide immediate, personalized feedback and detailed explanations, adjusting to the student’s pace.(38) Systems like Carnegie Learning’s MATHia utilize machine learning to adapt problems in real-time, offering challenges that are neither too easy nor too difficult.(39,40)
Educational Data Analytics: AI-driven data analytics tools are used to interpret vast amounts of educational data collected through various digital platforms.(21) These tools analyze patterns and insights from data on student behaviors, engagement levels, and learning outcomes, enabling educators to make informed decisions.(41,42) For example, platforms like Knewton provide analytics that help educators understand student performance trends and develop targeted interventions to assist at-risk students.(8,43)
Automated Response Systems: Leveraging natural language processing, automated response systems can answer student inquiries instantly, providing information and solving common issues without human intervention.(44,45) This not only enhances student satisfaction by providing timely responses but also reduces the workload on university staff.
Adaptive Learning Platforms: These platforms use AI to dynamically adjust content and assessments based on individual learning trajectories.(46,47) By continuously analyzing student responses, adaptive platforms can identify the most effective teaching methods and learning paths for each student, significantly improving learning efficiency.(47) Tools like DreamBox Learning offer mathematics education that adjusts in real-time, reflecting the student’s ability to grasp concepts.(46,48)
Impact of AI on Learning Behaviors
The integration of Artificial Intelligence (AI) in educational settings has markedly transformed the learning experience for university students, particularly through personalization, enhanced motivation, and improved retention.(49,50) This section discusses how AI influences these aspects of learning behaviors.
Personalization of Learning: AI facilitates a highly personalized learning environment by adapting educational content and methodologies to meet the individual needs of students.(51,52) Intelligent tutoring systems, adaptive learning platforms, and personalized learning environments analyze the ongoing performance and learning styles of students, tailoring the instructional content accordingly.(5,53) For instance, AI systems can modify the complexity of problems based on student responses or suggest additional resources to address specific weaknesses.(15,19,54,55) This customization helps in aligning educational experiences with individual learning curves, significantly enhancing learner engagement and efficacy.
Influence on Motivation: AI-enhanced learning tools can significantly boost student motivation by providing immediate feedback and recognizing achievements with digital badges and other rewards.(56,57) These systems create a dynamic learning environment that continuously engages students. AI-driven analytics also enable educators to identify and intervene when students show signs of decreased engagement or motivation, thus maintaining a high level of engagement throughout the course.(57,58,59) Moreover, the novelty and interactiveness of AI tools can increase curiosity and enthusiasm for learning, which are crucial motivators for student participation.
Effects on Retention of Information: The adaptive nature of AI in education helps in optimizing the retention of information.(5) By facilitating spaced repetition and revisiting key concepts at optimal intervals based on individual student data, AI systems ensure that knowledge is reinforced and retained.(58,60) Additionally, AI can identify the best teaching strategies for different types of content, thereby enhancing the overall retention rates.(61,62) For example, visual learners might receive more graphical content, while auditory learners might benefit from increased use of audio explanations.(63,64)
Impact on Learning Strategies: AI technologies also transform learning strategies by introducing sophisticated data-driven methods.(65,66) With big data analytics and machine learning, educational AI systems can uncover effective learning patterns and promote strategies that lead to better academic outcomes.(14,67) These might include recommending group studies when beneficial or suggesting individual study plans based on predictive performance outcomes.(68)
As AI continues to evolve, its role in enhancing learning behaviors through personalization, motivation, and retention is becoming increasingly significant.(19) The capability of AI to adapt to the individual learning journey not only enhances educational outcomes but also supports a deeper, more meaningful learning process.(3)
Impact on Psychological Well-being
The incorporation of Artificial Intelligence (AI) in university settings extends beyond educational outcomes, significantly influencing the psychological well-being of students.(2) This section explores the nuanced impacts of AI on student stress and anxiety, self-efficacy, autonomy, and highlights potential psychological risks associated with dependency on these technologies.
Stress and Anxiety: While AI tools are designed to enhance learning efficiency and adaptability, they can also be sources of stress and anxiety.(69,70) The pressure to keep up with AI-driven learning paces, constant monitoring, and evaluation through digital platforms may increase stress levels among students.(71) Furthermore, the impersonal nature of some AI interactions can exacerbate feelings of isolation, particularly when students are struggling with complex or challenging material without human support.(72) However, AI can also mitigate stress by providing students with instant feedback and additional learning resources tailored to their needs, potentially easing the anxiety associated with uncertainty in their academic performance.(71)
Self-Efficacy and Autonomy: AI’s ability to personalize learning experiences can have a dual effect on self-efficacy.(73) On one hand, adaptive learning technologies that adjust to individual learning styles and paces can enhance students’ confidence in their ability to master subjects at their own speed.(74,75) On the other hand, over-reliance on AI for learning guidance and decision-making can diminish a student’s sense of autonomy and self-driven learning, potentially impairing the development of independent problem-solving skills.(76,77) It is crucial for educational AI implementations to strike a balance, ensuring that while supportive, they still encourage students to take initiative and build their independent learning competencies.(78,79,80)
Psychological Risks: The dependency on AI technologies introduces several psychological risks.(81,82,83,84) The foremost concern is the potential for reduced human interaction, as AI takes over more aspects of the educational process.(16) This shift can affect social learning and interpersonal development, which are critical components of university education.(85,86) Additionally, the constant data collection and surveillance capabilities of AI tools can lead to privacy concerns and the fear of being constantly watched, which can be psychologically unsettling for students.(87,88)
Moreover, the predictive nature of some AI applications might pigeonhole students into specific learning paths, reducing their exposure to a broader curriculum and potentially stifling creativity and critical thinking by promoting a too narrow focus on predicted strengths or weaknesses.(89,90,91)
While AI has the potential to transform educational experiences positively by supporting personalized and adaptive learning, it is essential to remain vigilant about its psychological impacts. Institutions must implement AI tools thoughtfully, with considerations for their broad implications on student well-being. Ensuring that AI aids rather than hinders the development of critical life skills such as self-regulation, resilience, and interpersonal communication is vital for the holistic development of students.
Diversity and Equity in AI-Enhanced Education
Artificial Intelligence (AI) holds transformative potential for personalizing education and making learning more accessible.(92) However, its impact on diversity and equity within university settings is a subject of critical importance that warrants a thorough examination.(15) This section explores whether AI in education serves as a tool for bridging disparities or if it inadvertently perpetuates existing inequalities among students from diverse backgrounds.
Addressing Inequalities: AI technologies can potentially level the educational playing field by providing personalized learning experiences tailored to the needs of each student, regardless of their background.(15,93) For instance, AI-driven platforms can adapt to various learning styles and speeds, which is particularly beneficial for students who may not thrive under traditional teaching methods.(94) Programs such as ELLI (Enhanced Learning through Intelligent Algorithms) are designed to identify learning gaps and offer customized support, thereby potentially reducing the performance gap between students from varying academic and cultural backgrounds.(95,96,97)
Perpetuating Inequalities: Despite the potential benefits, there are significant concerns that AI might reinforce existing disparities.(97,98) One major issue is the digital divide—the gap between those who have access to modern information and communication technology and those who do not.(99) Students from lower socioeconomic backgrounds may lack access to the necessary hardware and broadband internet required to benefit from AI-based educational tools, thus exacerbating educational inequalities.(100,101) Additionally, AI algorithms are often only as unbiased as the data they are trained on; if the underlying data reflects historical biases, the AI’s outputs could perpetuate these biases, affecting minority students adversely.(102,103)
Accessibility of AI Technologies: Ensuring that AI educational tools are accessible to all students is crucial for fostering equity.(15,104) Accessibility involves not only physical access to technology but also the design of AI systems that are inclusive of students with disabilities and those who speak languages other than English.(105,106) For example, AI applications that incorporate multilingual support and are designed with user-friendly interfaces can help bridge the language barriers and improve usability for students with varying abilities.(107,108)
Recommendations for Enhancing Equity: To harness AI’s potential for promoting equity in education, policymakers and educational institutions must implement several strategic measures:(109)
1. Infrastructure Investment: schools and governments should invest in infrastructure that provides all students with high-speed internet and modern devices.(110)
2. Bias Mitigation: AI developers must prioritize the creation of algorithms that are transparent and free from biases. This involves using diverse datasets for training and regular audits for bias.(111)
3. Inclusive Design: AI tools should be designed with input from a diverse group of stakeholders, ensuring they meet the broad needs of various student groups, including those with special educational needs.(112,113)
While AI in education has the potential to significantly enhance learning outcomes and access, it also poses risks of deepening inequalities if not carefully managed. By addressing these challenges proactively, educators and technologists can ensure that AI serves as a force for equity and inclusion in higher education.
Diversity and Equity in AI-Enhanced Education
Artificial Intelligence (AI) in education promises significant advancements in personalized learning and operational efficiency.(114) However, its role in addressing or perpetuating inequalities among students of various socio-economic, cultural, and academic backgrounds remains a critical concern.(115) This section evaluates whether AI technologies in education serve as tools for fostering diversity and equity or whether they inadvertently deepen existing disparities.
Addressing Inequalities: AI can potentially democratize education by offering personalized learning experiences that adapt to the needs of each student, regardless of their background.(116) For example, AI-driven programs can provide additional support to students who may need more time to understand complex concepts, thereby leveling the playing field for those who might not have access to private tutoring.(117,118) Furthermore, AI can help non-native speakers by offering real-time language translation services, making educational content more accessible to students from diverse linguistic backgrounds.(119)
Perpetuating Inequalities: Despite these advantages, there are significant concerns regarding the accessibility of AI technologies.(120) Students from lower socio-economic backgrounds may not have reliable access to the necessary technology and high-speed internet required to benefit from AI-based educational tools, thus exacerbating the digital divide.(121) Moreover, if AI systems are trained on data that is not sufficiently diverse, their algorithms could perpetuate biases, thereby disadvantaging minority students.(122)
Accessibility of AI Technologies: Ensuring that AI tools are accessible to all students is a paramount concern.(80) This includes not only the physical availability of technology but also the design and functionality of AI systems to accommodate students with disabilities.(123) For example, AI tools must be developed with interfaces that are usable for students with various types of disabilities, such as visual or auditory impairments.(124)
Strategies to Enhance Equity: To maximize the benefits of AI in education and mitigate its risks, several strategies can be employed:
1. Infrastructure Development: educational institutions and governments should invest in infrastructure that ensures all students have access to the necessary technological tools and internet connectivity.(125,126,127)
2. Inclusive Design: AI systems should be designed with input from a diverse range of stakeholders to ensure they cater to the varied needs of different student groups.(128,129)
3. Bias Mitigation: it is crucial to train AI systems on diverse datasets and to implement regular audits for algorithmic bias to prevent the perpetuation of stereotypes and inequalities.(129)
DISCUSSION
As Artificial Intelligence (AI) continues to evolve and integrate within higher education, identifying gaps in existing research and proposing future directions is crucial for maximizing its potential benefits and addressing its challenges.(130,131) This section outlines several key areas where further research is necessary and anticipates possible technological developments in AI that could significantly influence higher education.
Identifying Research Gaps:
1. Equity and Accessibility: while AI has the potential to transform educational experiences, research into how it can be leveraged to bridge rather than widen the gap between different student demographics remains sparse.(132,133) Future studies need to focus on developing and evaluating AI tools that are accessible to all students, including those from diverse backgrounds and those with disabilities.
2. Ethical Implications: there is a need for more comprehensive research into the ethical implications of AI in education, particularly concerning data privacy, consent, and security.(134,135,136) Studies should explore how institutions can implement AI technologies in ways that respect student privacy and ensure data is used ethically and responsibly.(134,136)
3. Long-term Impact: the long-term impact of AI on student learning and psychological well-being is still not well-understood.(137,138) Longitudinal studies are necessary to assess how sustained use of AI affects learning outcomes, career preparedness, and the mental health of students.(10)
4. AI and Faculty Roles: research is needed to understand how AI is reshaping the roles and responsibilities of faculty.(139,140) This includes exploring how AI tools can enhance faculty effectiveness without replacing the essential human elements of teaching.(139)
Future Technological Developments:
1. Advanced Predictive Analytics: future developments in AI could enhance predictive analytics to more accurately identify students at risk of underperforming or dropping out ( 141,142 ). These systems could provide real-time interventions tailored to individual academic needs and personal circumstances.(141)
2. AI-Driven Personalization Engines: the next generation of AI personalization engines may use deeper insights into cognitive and emotional aspects of learning to tailor educational experiences more effectively.(142,143,144) This could involve technologies that adjust teaching methods based on real-time analysis of student emotions and engagement levels.
3. Augmented Reality (AR) and AI Integration: AR integrated with AI could create immersive educational environments that simulate real-world scenarios for fields such as medicine, engineering, and the arts, providing students with hands-on experience in a controlled, virtual setting.(145,146,147)
4. Blockchain and AI for Credentialing: blockchain technology combined with AI could revolutionize credential verification in higher education by providing secure, immutable records of student achievements and learning outcomes.(148,149) This integration could facilitate more transparent and efficient recognition of qualifications across borders.
CONCLUSIONS
The intersection of AI and higher education holds tremendous promise, yet it also presents significant challenges that require careful investigation and thoughtful intervention. By addressing these gaps and anticipating future technological trends, researchers and educators can help ensure that AI technologies serve as effective tools for enhancing educational outcomes and equity.
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CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.
FUNDING
Proyecto DIDE PF47.
AUTHORSHIP CONTRIBUTION
Conceptualization: Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández.
Data curation: Diana Catalina Velastegui-Hernández, Mayra Lucia Rodriguez-Pérez.
Formal analysis: Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández.
Research: Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández.
Methodology: Luis Fabián Salazar-Garcés.
Supervision: Luis Fabián Salazar-Garcés, Mayra Lucia Rodriguez-Pérez.
Validation: Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández.
Writing – original draft: Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández.
Writing – review and editing: Luis Fabián Salazar-Garcés, Diana Catalina Velastegui-Hernández, Mayra Lucia Rodriguez-Pérez.