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Association between mental health and academic performance among university undergraduates- The interacting role of lifestyle behaviors - PMC

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Cognitive AI computing advancements: Navigating the complexities of academic honesty.

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Artificial intelligence and academic integrity: striking a balance

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A look at how universities can encourage the ethical and transparent use of artificial intelligence tools to support learning while guarding against misconduct

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This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

1. Introduction

AI has been increasingly propagated as having strategic value for education [ 7]. Loeckx [ 8] suggested that AI could be an effective learning tool that lessens the burdens of both teachers and students and offers effective learning experiences for students. Coupled with current education reforms such as the digitalization of educational resources, gamification, and personalized learning experiences, there are many opportunities for the development of AI applications in education. For example, the modelling potential of AI techniques has been exploited systematically to develop reactive and adaptive tutorials for the construction of individualized learning environments as compensation for the shortage of teachers through the use of intelligent tutoring system (ITS) [ 10]. ITSs provide personalized learning experience in four main ways: monitoring student's input, delivering appropriate tasks, providing effective feedback, and applying interfaces for human-computer communication [ 7]. When more ITSs are created for more subjects and topics, it is likely to change the role of teachers, and hence, schooling may need to be reconceptualized. There exist many concerns and worries among teachers on if AI challenges their jobs. At the same time, such questions as what is being learned and how AI is being used are being discussed currently by researchers as well as by educational practitioners. Some researchers wondered whether advancements in AI would challenge or even replace teachers since many other jobs are being replaced by automation [ 11]. There is an emerging recognition that teachers' professional roles need to be adjusted as AI advances and this will trigger new organizational forms [ 12]. Emerging challenges also included students' attitudes towards these changes [ 13]. To some extent, students as digital citizens are able to leverage AI to improve learning outcomes. Nonetheless, they may fail to use suitable AI techniques appropriately for a specific learning context, which would result in negative attitudes towards learning [ 14].

To summarize, this research involves a review of the studies of AI in education. Previous studies have included three essential perspectives of AI in knowledge processing: (a) knowledge representation, (b) knowledge obtaining, and (c) knowledge derivation [ 3]; this review will focus on AI techniques and tools that have been integrated into education recently after the proliferation of AI. The "first generation" of AI could support human intellectual work by applying rule-based expert knowledge, and the "second generation" may find the optimal solution by statistical/search model, while the "third generation" will dramatically improve recognition performance based on the brain model. This review focuses on articles published in the period from 2010 to 2020 from the Web of Science, as that represents the period when the second and third generation of AI began to make headways into education. The research questions that guided this review are as follows:(1) What is the overall state of AI in education? Which research topics and research designs related to AI in education are evident from 2010 to 2020? (2) What are the trends in published studies in terms of AI in education? (3) What are the challenges generated from the current research of AI in education?

2. Method

This study is a systematic literature review. The objectives of the review were to analyze and interpret findings based on predefined research questions (see above) and criteria which serve to point out future directions [ 15]. The predefined research foci as shown in Table 1 are research purpose, learning subject, educational level, research approach, and effects. The review was conducted in three stages: planning, performing, and reporting the systematic review.

2.1. Planning the Review

As previous reviews about AI were conducted in the physical sciences [ 16, 17], the study aimed to conduct a review in the field of the social sciences.

The Web of Science database and the Social Science Citation Index (SSCI) journals were selected for the search for desired articles published from 2010 to 2020. Articles published in the SSCI database are generally considered as high-quality publication among education researchers. The keyword employed was "artificial intelligence," and the subject area was refined to "education and educational research". This process yielded 142 articles including 121 research articles, 10 review papers, one interview paper, and 5 book reviews. The selected articles include both analytic studies (primarily qualitative research) and empirical studies (primarily quantitative research).

2.2. Performing the Review

Following Wu et al. [ 18], this study was conducted in two steps: identification and coding. In the first step, an article was selected to the potential pool when it qualified for either of two criteria: (a) the research involved a specific AI technique as an intervention in assisting learning or teaching and (b) it provided empirical evidence or in-depth analysis. As already noted, only articles indexed in SSCI were considered. It should be noted that studies that focused on the development processes of AI without educational implications or only adopted AI as a learning subject without the employment of AI were excluded from this review. Second, as for the analytic studies, only studies that discussed the effect of AI techniques on education were included. Each full text of all the identified papers was read and screened individually by three-panel members with doctoral degrees or professorships in the field of learning technology. Studies that did not fit clearly with the criteria were brought up for panel discussion. The screening process yielded 100 articles out of the original set of 121.

In the second step, all the authors discussed thematic analysis principles and established a coding scheme in terms of how AI was used in education. Two main categories were investigated: research questions and technology adoption. Firstly, with regard to research questions, previous research has found three basic models of AI in knowledge processing: knowledge representation, knowledge obtaining, and knowledge derivation [ 3]. Building on that foundation, the research questions of the sample papers were classified into three dimensions: (a) development, focusing on the knowledge presentation model; (b) extraction, centering on how to obtain knowledge from data mining; and (c) application, emphasizing the human-computer interaction through information derivation. Secondly, with regard to technology adoption, the focus was on the types of technology that the study adopted, which were further categorized into software (e.g., algorithms and programs) and hardware (e.g., sensors and devices such as virtual reality). It should be noted that a study with technology without an AI purpose in education was not included. A detailed description is shown in Table 1 and it includes learning subject, educational level, research approach, and effects. Moreover, the researchers conducted further frequency comparisons on the associations between the research purposes and some factors such as AI technology adoption as well as time periods to predict the trends and challenges of AI in education.

3. Findings and Discussion

According to the above coding criteria and content analysis, the three dimensions of research questions are shown in Table 2 and the 72 studies from 63 empirical studies (5 papers have two studies and 2 papers have three studies) are further subclassified into 11 categories. There are 23 studies in the dimension of development. The AI technique was utilized as a development tool for the construction of a smart learning environment, which can be subclassified as focusing on the development of algorithms including classification, matching, recommendation, and deep learning for teaching and learning purposes. Additionally, 35 reviewed studies were found in the dimension of extraction, which referred to the application of developed AI techniques, normally based on algorithms, to offer students feedback, reasoning, and adaptive learning. 14 empirical studies were found in the dimension of application which consisted of affection computing, role-playing, immersive learning, and gamification. In the integration dimension, AI techniques included those involving human factors as vital variables to identify and analyze learners' personalized features. In such studies, human-computer interaction was generated to improve such characteristics as creativity, responsibility, and critical thinking that can impact learners' performances and perceptions. The following sections describe what educational issues were dealt with in the age of AI and how AI technique was employed in each research question.

3.1. Dimension of Development

As shown in Table 2, 16 empirical studies were found focusing on the development of education systems such as intelligent tutoring system (ITS) and electronic assessment. The development procedure was usually conducted with an induction-deduction approach, in which prior experiments and data were analyzed to predict the variables followed by the algorithm testing to obtain the final modelling equation [ 19].

Additionally, in terms of the matching/group formation modelling, prior research employing stereotype theory has assessed that the Bayesian networks, association rules, clustering, fuzzy C-means, and the fuzzy and genetic algorithms were well-accepted algorithms for the modelling of individual properties of the student. These techniques provide potential indications for the investigation of forming homogeneous and heterogeneous groups in an educational context [ 21].

Moreover, the trends of the growing amount of data challenge educators to analyze qualitative data efficiently. Natural language processing (NLP) provided a means to diagnose the problem and make a recommendation by simplifying and accelerating the discovery of what lies within the data [ 22]. However, the assessment of a complex educational system requires more profound information retrieval. The integration of multiple approaches, such as benchmark in NLP/Semantic Web field, was suggested to model smarter computer-aided systems in which agents could be trained automatically [ 23].

To optimize the modelling in the learning context, the hierarchical structures were considered as potential solutions to model the educational system. This is because education is generally a complex system with the exhibition of subsystems and components, in which the invisible causal processes among subsystem/component behaviours would causally affect each other [ 24]. It was suggested that systematic modelling should analyze three dimensions in the education context: learner's variation, learning domains, and learning activities [ 25, 26]. For example, some researchers constructed the higher-order item response theory framework involving the overall ability at the first dimension and multiple domain abilities at the second dimension, which has been well adopted in the automatic problem-solving process [ 27].

Based on the above and Nguyen and Yang's suggestion [ 28], the aims of developing an AI-integrated system in education could be grouped into four types: classification (5 studies), matching (3 studies), recommendation (5 studies), and deep learning (10 studies). (1) Classification refers to the reconstruction of knowledge bases, in which the materials could be categorized according to varied characteristics. Classification demarcates knowledge content, which contributes to the accuracy of text analysis [ 3]. For example, some researchers developed an ITS with the characteristics of categorizing motion problems, by which learners could easily access different types of motion problems in Mathematics [ 29]. (2) Matching refers to a conversion mechanism, in which varied sets of classification are connected to specific learning purpose. For example, a text-to-diagram system was developed for blind students to link geometry words to an underlying diagram on the Braille printout, which has been certified as an effective teaching/learning tool at a Blind school [ 30]. (3) The recommendation is regarded as an intelligent authoring tool. With the support of the natural language process, it could automatically create new themes, theories, and pedagogical contents as a response to learners' feedback, to help teachers save time and effort [ 31]. It constructed a human-computer interaction and widely used to generate real-time and intelligent feedback according to learners' input, which has been regarded as a reliable feature in modern assessment system [ 32]. (4) Deep learning, or machine learning, is a comprehensive approach of big data processing and learning behaviour analysis. Based on the proliferation of big data in education, such as learning or teaching behaviour, the system could self-adjust to meet users' dynamic requirements by upgrading its algorithms [ 33].

To date, some studies have reported the lack of significant impact on improving teaching. The challenge was largely attributed to the weak pedagogical design and lack of appropriate assessment criteria [ 8]. Future research should therefore be grounded in learning theories so that more acceptable, accessible, and efficacious AI can be an integral part of learners' lives.

3.2. Dimension of Extraction

Educators have begun to explore suitable applications of AI techniques in their teaching. There are currently some AI applications that have achieved the integration of technique, domain knowledge, and pedagogical design. The three types of pedagogical applications of AI identified in this review were feedback (16 studies), reasoning (10 studies), and adaptive learning (9 studies). While these applications could be interlinked, they were categorized as such based on the classification explicated by the authors of the reviewed articles.

3.2.1. Feedback

One of the challenges impairing personalized learning is the inappropriate sequencing of contents. The restructuring of presentation sequences is seeking a way to redefine the organization of knowledge according to the student's reaction. In this situation, feedback is an important approach to meet learners' proximal learning patterns [ 9]. Using an artificial neural network, the system provides immediate feedback according to students' input to help them gradually get access to the abstract concepts and perform practical exercises. Besides, researchers perceived a positive trend towards the system, which may attribute to two perspectives.

Although a large number of benefits were reported with respect to automated feedback of domain knowledge, no research in this review had established the connection to pedagogical theories. Most of the authors in the development dimensions were from the computer science domain, which leads to their focus on the presentation of source data (domain knowledge) technically without much pedagogical consideration.

3.2.2. AI-Supported Reasoning

The recursive feedback may have the potential to foster learners' abilities to reason in specific ways because the human-computer interaction is able to engender among the students a sense of responsibility toward improving the construction of knowledge repository [ 39]. The reconstruction of the knowledge repository was seen as a process of using modelling to realize pedagogical design as shown in Figure 1(b). However, some researchers found that novices such as students and preservice teachers showed minimal understanding of the invisible causal behaviours in the system compared to experts and experienced teachers [ 24]. Another research showed a similar conclusion: students were able to learn the relevant facts and pairwise relations, while they may still fail to reason with them very well [ 39]. One possible explanation could be that reasoning is largely invisible and it is difficult to induce the processes of reasoning through the observation of the behaviours. AI techniques such as the visualization technique could be applied to foster learners' reasoning.

To help learners improve their reasoning, the graph structure [ 29] and learners' engagement [ 24] techniques have been studied. For the graph structure, intelligent systems could be developed to make thinking visible. In a sense, the simulation approach of the AI technique was employed to mimic thoughts tracking the reasoning visually in real time. For example, the argument-mapping tools were designed to assist learners with visualization of the premises and conclusions of arguments. The findings showed that a sequence of connected arguments was chained together for learners to make an ultimate conclusion [ 40]. Drawing from the sociocultural theories of learning in designing AI to support students' reasoning, Vattam et al. [ 24] reported that engaged learners could better understand the multiple levels of organization in complex systems. Therefore, students' engagement is an essential aspect to be considered for the design of a learning system that aims to support reasoning.

However, using AI techniques, including visualization and hierarchical reasoning modelling, may be inadequate to support reasoning. The four studies reviewed focused on the utilization of modelling to support general reasoning, while the reasoning model should be largely domain-specific [ 24, 39, 40, 42]. Moreover, there is an unresolved challenge in coding learners' behaviours as far as AI-supported reasoning is concerned. The reasoning process may be more effective when learners' personalized performance is considered. Although the visualized reasoning tools could perform well in a small-scale group setting, it is difficult to obtain adequate reasoning analysis of the data from a large population because the reasoning system fails to adjust itself automatically. Therefore, the requirements of dealing with increasingly large and diverse data demand self-adaptive alternatives [ 9].

3.2.3. Adaptive Learning

Previous research has emphasized that the design dimension was a worth exploring alternative in the application of adaptive system [ 46]. To design successful adaptive systems in education, curriculum designers and system designers have to leverage on to include the modelling of the problem-solving process in the specific domain knowledge and the use of big data [ 21, 44]. Firstly, the mechanism of the adaptive system connects learners' prior domain knowledge and the evaluation of their current domain performance to scaffold their problem-solving [ 47]. In particular, the pedagogical design is essential in adaptive intelligent context. It involves the selection of adaptive algorithms and considerations about the compatibility of the learning style and the intelligence supportive methods. In this sense, the assumption that AI would threaten the teachers' position may be unfounded because of teachers' vital role as curriculum designers. Secondly, the adaptive system is empowered by big data. Since the main feature of the adaptive learning system is personalization, accumulation of big data such as the range of diverse individual characteristics and learning style and preferences is necessary for intelligent personalization to be realized. However, research on personalization in the context of the adaptive system is limited to the users' characteristics related to domain knowledge. The deeper internal characters, such as human mental status and creativity, were barely noticed and studied [ 21]. This however has vital research potential with the development of advanced AI techniques such as biofeedback techniques.

3.3. Dimension of Application

3.3.1. Technology Adoption in the Application Dimension

According to the content analysis of the selected papers shown in Table 1, there are five typical AI techniques that supported affection computing and analysis in the education sector. They are complex algorithms, visualization, XR (virtual/augmented/mixed reality), wearable technique, and neuroscience. In many situations, they supported each other to construct a smart learning environment and system. (1) Complex algorithms were designed with consideration of human factors rather than the simple combination of functional blocks. From the perspective of human-computer interaction, the learners should be treated as a knowledge creator rather than the receiver, which helps to generate positive affection status. From the perspective of presentation modes, the traditional declarative statements in a computer system should be replaced by more diversified verbal presentations such as dialogue, coaching, and generality. (2) Visualization was seen as an optimal method chosen for the solution of complex conception. One of the benefits of visualization is making complex knowledge entertaining, such as game-based learning, in which learners' motivation will be greatly generated. (3) XR including virtual/augmented/mixed reality provides a highly simulated learning context, which may be challenging to realize in physical classrooms. For example, to help learners understand complex landforms in geography, XR indulges students into a lively and creative status. (4) The wearable technique, such as Google glasses, helps to integrate learning activity into somatosensory moves. Although it was still in an exploratory period, it has great potential to advance domain knowledge in a practical context in daily life. (5) Modern neuroscience exploits how the brain works and this expands the research of learning to include the learners' physiological state. Research in this area would enrich understanding about individual variations and could provide additional avenues to match instruction with the most optimal guidance.

3.3.2. The Categories of the Application Dimension

With the supports of the above five AI techniques, four types of learning models were generated with the application of affection analysis, which was biofeedback (6 studies), role-playing (2 studies), immersive learning (2 studies), and gamification (4 studies).

Affection computing refers to the analysis of human emotions and feelings captured by physical sensors and affective algorithms, which has gained much attention in recent years. Affection computing enhanced human-computer interaction. Based on the facial identification, some researchers improved the intelligent tutoring system by which students' emotional status was detected to give them timely emotional feedback [ 51]. Two essential aspects are needed to optimize the affection computing technique: first, teachers have to make timely appropriate instructional adjustments according to learners' affective status; second, comprehensive operation of multimode affection sources as a single source is unlikely to provide accurate analysis of affection. For example, the eye-tracking technique could capture learners' eye fixation to track the attended area, but the reasons for the foci may be attributed to different affections such as interest, anxiety, or even distraction. An additional source of data such as EEG could help to make a more accurate assessment [ 52].

3.4. The Results from Qualitative Research

4. The Research Trends of AI in Education

4.1. Technology Adoption of Internet of Things

4.2. Swarm Intelligence in Education

4.3. Deep Learning and Neurocomputation

4.4. Evaluation of AI in Education

All empirical studies reviewed presented the positive effects of AI techniques on education (see Table 1). However, the interview and the review paper have, respectively, surfaced the challenges or misunderstanding of AI in education [ 4, 21]. There is a need to articulate a holistic evaluation criterion to measure the effectiveness of AI in education. To ensure the validity and reliability of the evaluation, a multidimensional model should be adopted, which includes technique, pedagogical design, domain knowledge, and human factors. Woolf's [ 119] Roadmap for Education Technology predicted that in the era of AI Educational Data Mining, the lifelong assessment of students' knowledge, their progress, and the environments where they learn, as well as the success and failure in teaching strategies, can be chronologically tracked.

Besides, current research is disproportionately focused on specific educational contexts and a handful of variables. As shown in Figure 2, most research sampled students as participants, while teachers and professor practitioners were less noticed; additionally, most researchers considered science, humanity, and social science as subjects, but less attention was paid to sports, arts, and special education. For example, only one study was found to develop text-to-diagram conversion as a novel teaching aid for blind learners [ 30].

5. The Challenges AI Confronted in Education

AI is a promising field that faces many technology bottlenecks. The challenges would be more complex and intricate, especially when they are connected to an application in education. The challenges this review identifies could be classified into three categories: technique, teachers and students, and social ethics.

Another great challenge reported in the Horizon report in 2018 is the reconceptualization of the role of educators. Teachers' attitudes towards AI have a significant influence on the effectiveness of using AI in education. Teachers may swing from total resistance to overreliance. The former could arise from inadequate, inappropriate, irrelevant, or outdated professional development. The latter may be due to teachers' unrealistic expectations. These teachers may focus too much on the emerging AI technologies rather than learning itself [ 44]. Additionally, from the perspective of students, AI technique may provide smart and efficient tools that cause students to avoid doing the knowledge processing work that teachers expect them to do. For example, the AI translators may offer ready-made illustrations, pronunciation, fixed phrases, and even a serial of examples. Students are thus unwilling to engage in the inquiry processes that facilitate deep learning.

6. Conclusions

Given the rapid growth of AI, there is an urgent need to understand how educators can best utilize AI techniques for the academic success of students. This paper reviewed AI in education research from 2010 to 2020. It is found that the research to date could be classified into three dimensions: the dimension of development including classification, matching, recommendation, and deep learning; the dimension of an extraction involving feedback, reasoning, and adaptive learning; and the dimension of application including affection computing, role-playing, immersive learning, and gamification. Moreover, based on the research questions and the related AI techniques, four research trends were identified. They are the Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of the effect of AI in education. The challenges of AI in education were also conspicuously seen in terms of technique perspective, teachers' and students' roles, and social ethical issues. These findings could be valuable references for educational researchers, students, and AI developers who plan to contribute to the relevant studies. Furthermore, it seems clear that educators need to work with AI engineers to address the gaps between technique and pedagogy.

7. Limitations and Future Study

Although this review does propose some valuable trends and potential research directions for AI in education, there exist several limitations. Firstly, the papers reviewed in this study were filtered from Social Science Citation Index, while other databases on natural science (e.g., SCOPUS and EI) and sources (e.g., reports, news, conference papers, and patents) could be involved to offer a more comprehensive overview in this field. For instance, articles from the International Journal of Artificial Intelligence in Education that has published 30 volumes were not considered. This review therefore is limited only to SSCI articles. Additionally, the initial search could be extended using more keywords such as adaptive learning and tutor system, which may lead to the latest technical reports of AI in education that were not included in this paper. Secondly, since the current review was not attempted to be inclusive but to provide a systematic overview of AI in education, the analysis in this review may provide a framework for future research integration. For example, a more formal meta-analysis could be conducted on selected empirical studies that reported effect sizes to see what impact on learning AI might be having. Besides, the future analysis could go back further in time to see if there were changes about the time that AI 2.0 started to make headways into education.

Data Availability

The content analysis data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Acknowledgments

This research work was supported by the 2020 Humanities and Social Science Projects of the Ministry of Education (Grant ID: 20YJC880118), National Science Funding of China (Grant ID: 61977057), 2019 National Social Science Funding of China (19ZDA364), and the project of Informatization Capability in University Governance System, Chinese Association of Higher Education, 2020 (Grant no. 2020ZDWT18).

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A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

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This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education.

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Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act, and helps determine how we handle stress, relate to others, and make choices.

Mental health is important at every stage of life, from childhood and adolescence through adulthood. Over the course of your life, if you experience mental health problems, your thinking, mood, and behavior could be affected.

Mental Health Conditions

Mental illnesses are disorders, ranging from mild to severe, that affect a person's thinking, mood, and/or behavior. According to the National Institute of Mental Health, nearly one-in-five adults live with a mental illness.

Many factors contribute to mental health conditions, including:

Biological factors, such as genes or brain chemistry

Life experiences, such as trauma or abuse

Family history of mental health problems

Some mental health topics include:

A Serious Mental Illness (SMI) is a mental illness that interferes with a person's life and ability to function. Despite common misperceptions, having an SMI is not a choice, a weakness, or a character flaw. It is not something that just "passes" or can be "snapped out of" with willpower.

View SAMHSA's Public Message about Serious Mental Illnesses.

Early Warning Signs & Symptoms

Not sure if you or someone you know is living with mental health problems? Experiencing one or more of the following feelings or behaviors can be an early warning sign of a problem:

Eating or sleeping too much or too little

Pulling away from people and usual activities

Having low or no energy

Feeling numb or like nothing matters

Having unexplained aches and pains

Feeling helpless or hopeless

Smoking, drinking, or using drugs more than usual

Feeling unusually confused, forgetful, on edge, angry, upset, worried, or scared

Yelling or fighting with family and friends

Experiencing severe mood swings that cause problems in relationships

Having persistent thoughts and memories you can't get out of your head

Hearing voices or believing things that are not true

Thinking of harming yourself or others

Inability to perform daily tasks like taking care of your kids or getting to work or school

Do you think someone you know may have a mental health problem? Talking about mental health can be difficult. Learn about common mental health myths and facts and read about ways to help you get the conversation started.

Tips for Living Well with a Mental Health Condition

Having a mental health condition can make it a struggle to work, keep up with school, stick to a regular schedule, have healthy relationships, socialize, maintain hygiene, and more.

However, with early and consistent treatment-often a combination of medication and psychotherapy-it is possible to manage these conditions, overcome challenges, and lead a meaningful, productive life.

Today, there are new tools, evidence-based treatments, and social support systems that help people feel better and pursue their goals. Some of these tips, tools and strategies include:

Stick to a treatment plan. Even if you feel better, don't stop going to therapy or taking medication without a doctor's guidance. Work with a doctor to safely adjust doses or medication if needed to continue a treatment plan.

Keep your primary care physician updated. Primary care physicians are an important part of long-term management, even if you also see a psychiatrist.

Learn about the condition. Being educated can help you stick to your treatment plan. Education can also help your loved ones be more supportive and compassionate.

Practice good self-care. Control stress with activities such as meditation or tai-chi; eat healthy and exercise; and get enough sleep.

Reach out to family and friends. Maintaining relationships with others is important. In times of crisis or rough spells, reach out to them for support and help.

Develop coping skills. Establishing healthy coping skills can help people deal with stress easier.

Get enough sleep. Good sleep improves your brain performance, mood and overall health. Consistently poor sleep is associated with anxiety, depression, and other mental health conditions.

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Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act, and helps determine how we handle stress, relate to others, and make choices.

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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4641787

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https://www.hindawi.com/journals/complexity/2021/8812542/

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Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks - International Journal for Educational Integrity

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This paper connects the problem of artificial intelligence (AI)-facilitated academic misconduct with crime-prevention based recommendations about the prevention of academic misconduct in more traditional forms. Given that academic misconduct is not a new phenomenon, there are lessons to learn from established information relating to misconduct perpetration and frameworks for prevention. The relevance of existing crime prevention frameworks for addressing AI-facilitated academic misconduct are discussed and the paper concludes by outlining some ideas for future research relating to preventing AI-facilitated misconduct and monitoring student attitudes and behaviours with respect to this type of behaviour.

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https://philarchive.org/archive/ORAAII

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How mental health affects learning for students

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Understanding and prioritizing mental health is crucial for fostering overall well-being and resilience in individuals and communities alike.

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Menna Nabil 240349 A-9

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Academic Integrity considerations of AI Large Language Models in

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Association between mental health and academic performance among university undergraduates- The interacting role of lifestyle behaviors - PMC