The blue sky of AI-assisted language assessment: autonomy, academic buoyancy, psychological well-being, and academic success are involved

Abstract

Artificial intelligence (AI) transforms the educational landscape by radically changing how lessons are taught and students are evaluated. In light of these facts, it is critical to consider learners’ emotional and mental wellness as the building blocks of education. Given these facts, it is imperative to consider learners’ emotional and mental well-being as the fundamental components of education. The key goal of this investigation was to investigate the relationships between learner autonomy (LA), academic buoyancy (AB), psychological well-being (PW), and academic success (AS) in Intelligent Computer-Assisted Language Assessment (ICALA). The current study was conducted on a cohort of 391 students registered in courses at private language institutions in Al-Kharj, Saudi Arabia, who were at the intermediate proficiency level. The following instruments were administered: the Academic Buoyancy Scale, the learner autonomy instrument, the learner psychological well-being, and the academic assessment. Then, to evaluate data and quantify the interaction between LA, AB, PW, and AS, confirmatory factor analysis (CFA) and structural equation modeling (SEM) were applied. The findings underscored the significance of LA and AB in achieving a balance in the PW of EFL pupils, particularly when utilizing ICALA for language acquisition. Additionally, the instructional implications of this study and potential avenues for future research are extensively investigated.

Introduction

AI is a rapidly developing subfield of computer science that aims to create intelligent computers that can perform tasks that humans typically perform. In healthcare, finance, and transportation, AI technology is showing signs of becoming more widespread. McCarthy was the first person to present the concept of AI, which he characterized as the science and engineering of creating intelligent machines (Feng & Law, 2021). The effectiveness, precision, and decision-making powers of AI have the potential to make a revolution in a variety of different sectors. Using machine learning algorithms, AI can gain information and improve its performance (Babnik et al., 2022). These algorithms make it possible for robots to analyze large datasets, recognize patterns, and discover insights beyond human cognition’s capabilities.

AI involves creating computers and software capable of doing jobs that have historically required the intellect of humans (Fu, 2023). These activities simulate human cognitive processes, including conceptualization, resolving problems, recognizing patterns, and prediction induction (Heeg & Avraamidou, 2023). Incorporating AI into learning environments is a significant development that has altered the evolution of instructional techniques and learner participation. For students of EFL, who typically face daunting hurdles while completing their tasks, the use of technology in the educational system has become more important. These students often confront many challenges, including the need to grapple with language intricacies and adhere to academic writing norms or speak fluently, all while striving to explain advanced ideas within the context of their studies. These EFL students are facing a challenge that is not only educational but also technical.

As a consequence, there has been a significant focus on the potential of AI, particularly language-based models such as ChatGPT, to mitigate the effects of these issues (Degraeuwe & Goethals, 2022; Gallacher et al., 2018; Heeg & Avraamidou, 2023; Heift, 2021). It indicates a key moment in which educational technologies are entangled with the framework of learning techniques, becoming necessary rather than complementary. The growth of AI in this domain reflects a larger trend of digital integration. ChatGPT plays a significant role for EFL students by enabling them to efficiently analyze and utilize large amounts of information to provide valuable assistance throughout language learning and practicing writing (Smutny & Schreiberova, 2020).

In the field of language learning and assessment technologies, ICALA is a notable breakthrough. Through AI and adaptive learning algorithms, ICALA offers customized evaluations that specifically address each student’s requirements and knowledge levels. ICALA not only modifies the evaluation process but also facilitates learners in enhancing their language proficiency more efficiently (Abdullaeva et al., 2024; Heift, 2021). In light of the growing incorporation of technology in education, ICALA enables a more dynamic and captivating learning environment, which is especially pertinent for EFL learners who often encounter difficulties in conventional assessment contexts (Weng & Chiu, 2023).

The ICALA system’s ability to deliver flexible and prompt feedback may foster an atmosphere in which learners experience more autonomy and empowerment. This technical framework is compatible with modern educational ideas prioritizing learner-centered methodologies, such as constructivism and connectivism. According to constructivist theories, learners actively create knowledge by engaging with its content, but connectivism emphasizes the significance of networks and digital technologies in promoting learning (Degraeuwe & Goethals, 2022; Namaziandost & Rezai, 2024a). Such frameworks propose that when EFL learners interact with intelligent assessment technologies, they are not just passive information users but actively participate in their learning processes, increasing their independence and confidence.

Applying technology in education, particularly AI, may affect how students feel or act. According to Benson (2001), learners who can take charge of their language acquisition, make well-informed judgments, and direct their learning trajectory are considered autonomous. Autonomous learners take the initiative to plan their education, gather relevant information, and assess their performance (Little, 1991). To help students become more self-reliant, regulate their learning, and become lifelong learners, it is crucial to promote their autonomy as learners (Mohammadi Zenouzgah et al., 2023; Ritonga et al., 2023). Adapting to different learning situations, using real language resources, and being an active member of language communities both in and out of class are all things that autonomous learners are excellent at (Louis, 2006). Furthermore, LA allows students to follow their own interests and learning methods, which results in language acquisition that is more relevant and tailored to their needs (Benson, 2001). By creating safe spaces for students to make decisions about their learning, guiding and supporting them as they do so, and encouraging a mindset of continuous improvement via reflection and evaluation, educators play a pivotal role in empowering students to become agents of their learning (Ludwig & Tassinari, 2023; Namaziandost & Heydarnejad, 2023).

An individual’s buoyancy might be seen as an assessment of their ability to recover effectively from setbacks, which is another considered variable in this investigation. AB is a psychological concept that refers to a student’s ability to effectively overcome typical academic challenges (Jahedizadeh et al., 2018). Resilience is distinct from buoyancy as it is more closely associated with severe adversity or obstacles to growth, such as extended isolation and self-imposed limitations in an educational setting (Martin et al., 2013). As Yun et al. (2018) and Alazemi et al. (2023) highlighted, AB may increase when they are actively involved and intrigued by the topic. They feel that instructors have an essential role in influencing the educational outcomes achieved by students. In the same line of inquiry, Nurjamin et al. (2023) found that learner-oriented assessment strongly depends on AB and reflective thinking. Buoyant students actively strive to enhance their mental well-being in the face of challenges, leading to psychological growth (Martin & Marsh, 2006, 2008).

Taking a similar path, Zheng et al. (2023) found that the capacity of learners to manage their concentration and attitudes is crucial for estimating the amount of language proficiency attained by students who participate in online training utilizing educational technology. Fu (2023) performed a study and discovered that EFL learners experience more buoyancy with efficient teacher support. It is from the field of positive psychology that AB originated (Xu & Wang, 2022). According to MacIntyre and Gregersen (2021), positive psychology believes that language teaching and learning should emphasize positive and self-help characteristics to speed up the language learning and teaching processes. As stated by Gregersen (2013) and Alazemi et al. (2023), positive psychology perspectives provide deep meanings to language learning and teaching and support for their mental and psychological activities.

Many theoretical frameworks may elucidate the links among LA, AB, PW, and AS in the context of ICALA. The self-determination theory (SDT) is a pertinent theory that suggests persons possess essential psychological demands for autonomy, competence, and relatedness (Deci & Ryan, 2000; 2017). Within the realm of EFL acquisition, learners’ autonomy is heightened when they are given deliberate control over their learning trajectories via ICALA. Autonomy in language learning promotes intrinsic motivation, which is essential for maintaining long-term involvement and effort. Another relevant theory is Bandura’s social cognitive theory, which highlights the significance of self-efficacy in learning and achieving desired outcomes (Bandura, 1997). EFL learners with a significant degree of autonomy and buoyancy are more likely to cultivate a stronger sense of self-efficacy, which is the belief in their skills to attain academic success (Patra et al., 2022). An enhanced sense of self-efficacy may result in greater motivation and improved academic achievements, especially when using adaptive assessment systems that provide customized feedback and resources.

Empirical evidence suggests a favorable association between learner autonomy and psychological well-being. Enhanced autonomy in decision-making among EFL learners leads to increased satisfaction and reduced anxiety levels (Keller et al., 2022). In this particular setting, the notion of academic buoyancy, which pertains to the capacity of students to manage and overcome ordinary obstacles effectively, is crucial. Students who can recover from academic obstacles are more likely to keep a happy attitude and are less prone to experiencing negative emotional states that may impede their cognitive development. Furthermore, the incorporation of ICALA strengthens this connection. Through the provision of an accommodating and flexible learning environment, these technologies assist learners in efficiently navigating obstacles. For example, research has shown that students who make use of technology-enhanced learning platforms have increased levels of involvement and overall satisfaction (Alharbi & Alshammari, 2023). Such a pleasurable experience may alleviate the stress linked to language acquisition and evaluation, finally resulting in enhanced academic achievement.

Within the framework of ICALA, the prompt feedback and tailored learning plans offered by intelligent assessment systems may greatly improve learners’ comprehension of their areas of proficiency and areas for improvement. In addition to facilitating skill improvement, this feedback loop enhances a learner’s general confidence and drive, strengthening the positive cycle of autonomy, buoyancy, and academic achievement. To summarize, the interaction between the autonomy of EFL learners, their academic buoyancy, psychological well-being, and academic development within the ICALA framework emphasizes the need to establish supporting and empowering learning settings. By using technology progress and basing pedagogical methods on pertinent educational theories, educators may cultivate a comprehensive strategy that improves learners’ involvement and achievement in language acquisition.

The aims of the current research

Although LA and AB are increasingly being paid attention to in educational research, the literature on their impacts, especially in the context of ICALA for EFL learners, still lags somewhat. Few studies have examined how autonomy and buoyancy interact to affect PW and ASA in technology-enhanced language learning contexts, whereas earlier studies have examined them individually. Research that has already been published often ignores EFL students’ particular difficulties in adjusting to computerized evaluation instruments. Many studies have evaluated technology’s effect on language acquisition or concentrated on overall learner populations without exploring the psychological aspects influencing EFL learners specifically. This lack of clarity creates a knowledge vacuum about how these ideas interact in a setting where language learners have to negotiate the complexity of language learning and the requirements of tests driven by technology.

Furthermore, the previous research does not include actual investigations linking these ideas straight within the context of modern educational technology. Investigating how LA and AB could be used to improve PW and AS becomes more important as technology develops. This study seeks to close these gaps by utilizing empirical data and theoretical insights on the interaction of these variables and, therefore, provide a more complete knowledge of the elements influencing successful language acquisition in digital environments. By filling in these gaps, the research will enhance the scholarly debate on EFL learning and technology and have pragmatic consequences for developers and teachers in establishing favorable and efficient learning environments that give PW top priority along with AS. While the research gap emphasizes the necessity of focused studies on the psychological aspects of technology-enhanced language instruction, overall, the relevance of this study resides in its ability to guide educational practices and add to the body of knowledge on EFL learning.

The study seeks to promote the broader use of ICALA by advising educators and institutions on appropriately integrating these technologies into assessment techniques. This encompasses both technological issues and instructional strategies tailored to the distinct requirements of various learners. Furthermore, psychological well-being is crucial for successful learning, and this research aims to emphasize the potential of ICALA in cultivating a supportive environment for students. An affirmative emotional state may augment cognitive functions, facilitating pupils’ acquisition and retention of new language skills. The current study demonstrates, via path analysis, that intelligent assessment tools may improve learning outcomes, highlighting the need to incorporate these technologies into educational techniques to elevate student accomplishment.

Furthermore, understanding the elements that influence academic achievement may assist educators in identifying at-risk students and implementing focused interventions. This research emphasizes the essential function of ICALA in improving the language learning experience by concentrating on these interrelated objectives. It underscores the significance of cultivating autonomy, buoyancy, psychological well-being, and academic achievement, thus enhancing a comprehensive approach to language teaching. This study will inform educational practices and promote future investigation of novel technology in language assessment. This knowledge will be essential for teachers seeking to develop more successful curricula that address individual student requirements. The capacity to customize learning experiences according to student choices and achievements may greatly enhance learner engagement and accountability in their educational pursuits. In light of these objectives, the following research question was developed:

RQ: Do EFL learner autonomy and buoyancy improve their psychological well-being and academic improvement in ICALA?

Materials and methods

Participants and procedure

The present analysis was conducted on a group of 391 students who were enrolled in courses at Prince Sattam Bin Abdulaziz University in Al-Kharj, Saudi Arabia, and were at the intermediate level. Two-hundred females and 191 males used ChatGPT in their online instruction and assessment. The participants’ ages ranged from 19 to 29 years old. The participants were well qualified to answer the questions in English. The inquiry began in March 2023 and continued until December of the same year. The treatment was carried out using a web-based system connected to the Internet. A Kolmogorov–Smirnov (K-S) test was performed to determine whether the data follows a normal distribution. Upon this investigation, it was found that the data exhibited a typical trend. Therefore, CFA and SEM statistical methods were used to analyze the data. The analyses were conducted using the LISREL 8.80 program.

Measures

In this research, the respondents completed the measurements described below. Internal reliability coefficients for all applied measurements are presented in Table 1.

Table 1 The result of internal consistency

The participants’ opinions regarding AB were assessed using the Academic Buoyancy Scale (ABS), designed and confirmed by Jahedizadeh et al. (2019). The 27 questions that formed this exam evaluated 4 dimensions of second-language buoyancy: sustainability, regulatory flexibility, positive personal eligibility, and acceptance of academic life. Furthermore, ABS relies on a 5-point Likert scale, wherein 1 signifies a strong disagreement and 5 a strong agreement. In line with Cronbach’s alpha, which varied from 0.847 to 0.918, the ABS dependability in this investigation was good.

The learner autonomy questionnaire (LAQ), developed by Zhang and Li (2004), was used to determine the level of autonomy students experienced. This questionnaire comprises 11 questions, and its answer format is based on a Likert scale with 5 individual points. The researchers determined that the results were good (a = 0.920) after evaluating the instrument’s internal consistency after doing the evaluation.

The Psychological Well-Being Scale (PWB S) assessed the participants’ PW. PW-B S was developed by Ryff (2014) and Babnik et al. (2022); the initial iteration comprised 6 dimensions, each containing 20 items. In conducting this investigation, the condensed version recommended by van Dierendonck (2004) was utilized. The 6 scales comprised a total of 39 items, which were as follows: self-acceptance, positive interpersonal relationships, environmental mastery, life purpose, personal development, and autonomy. A 6-point rating system was employed for each scale, where a score of 1 indicated total disagreement and a score of 6 indicated total agreement. Internal consistency of PWB S was deemed satisfactory within the range of 0.844 to 0.921.

The present study researchers created a test to match the material discussed in the applicable resources. The evaluation consists of 32 questions for listening, speaking, reading, and writing skills. Experts’ knowledge was used to assess the items’ validity in terms of face and content. Two psychometricians and two EFL instructors were tasked with evaluating the academic quality of the questions as part of the assessment procedure. Afterward, the indicated test was given to a group of 34 EFL students who had commonalities with the target demographic to evaluate the reliability of the retests. To assess the consistency of the results over time, the same test was repeated on the same person after a 2-month interval. The Pearson correlation values demonstrated a statistically significant and robust test–retest reliability of the test (r = 0.86, p < 0.05).

Results and discussion

This section presents the reports produced from the data analysis and detailed explanations for each component. The first phase examines descriptive data about different aspects of each instrument, as shown in Table 2.

Table 2 Results of descriptive statistics

The variables of sustainability, regulation adaptability, and positive personal eligibility have comparable average values, indicating a somewhat equitable view across survey participants in these domains, with average scores ranging from 22.2 to 23.7. These findings suggest that the participants typically have a relatively positive mindset towards their skills, flexibility in sustainable behavior, and personal suitability. Furthermore, AB distinguishes itself by achieving the highest average score of 89.8, indicating a notable degree of resilience and flexibility in academic environments. These findings suggest that the participants had the necessary skills and knowledge to manage academic difficulties effectively, a positive sign for their general academic achievement. The ratings for language abilities, namely listening, speaking, reading, and writing, are with average values ranging from 3.5 to 4.1. Furthermore, the average score for PW is 79.3, suggesting a mostly favorable psychological condition among the individuals. These findings indicate that while facing difficulties in language proficiency, the participants experience a significant degree of psychological well-being, which may serve as a safeguard in their academic and personal spheres.

The data was then analyzed using the K-S test to detect any abnormal patterns. The outcome is shown in Table 3.

Table 3 The results of the K-S test

Table 3 shows that all scales and subscales had statistically insignificant values. This discovery may indicate that parametric procedures are best suited for data analysis.

The Pearson product-moment correlation was subsequently employed to investigate the relationship between LA, AB, PW, and AS.

Based on the information provided in Table 4, LA, AB, PW, and AS are correlated. The details are provided in Table 5.

Table 4 The correlation coefficients between LA, AB, PW, and AS

Table 5 The reports of correlation coefficients between subscales

A causal analytic framework and SEM were used to study the interactions between LA, AB, PW, and AS. The statistical analysis was carried out using LISREL 8.80. Some measurements were used to evaluate the concordance between the model and the data, including the magnitude of the chi-squared statistic, the root-mean-squared error of approximation (RMSEA), the goodness-of-fit index (GFI), the normed fit index (NFI), and the comparative fit index (CFI).

Table 6 exhibits the results, indicating that all degrees of fitness for Model 1 are appropriate. The values include the chi-square/df ratio (2.740), RMSEA (0.067), GFI (0.913), NFI (0.925), and CFI (0.934). Table 6 demonstrates that the requirements of Model 2 were satisfied, indicating a good agreement. The criteria being assessed are the chi-square/df ratio (2.330), RMSEA (0.065), GFI (0.945), NFI (0.938), and CFI (0.961).

Table 6 Model fit indices

Table 7 offers further details on the subject, while Figs. 1 and 2 graphically show the components’ connections. A significant relationship between AB and PW (β = 0.78, t = 15.31) and AS (β = 0.69, t = 13.20) is shown in the standard estimates and t-values. There is a significant correlation between the standard forecast and the t-values, which indicate a significant relationship between the LA and PW (β = 0.54, t = 10.15) as well as AS (β = 0.44, t = 5.93).

Table 7 Highlights from Model 1’s outcomes

Fig. 1

The graphic representation of the path coefficients in Model 1

Fig. 2

Significance of path coefficient T-values for Model 1

According to the findings in Fig. 3 and Fig. 4 as well as Table 8, there exists a noteworthy and favorable correlation between AB and the subsequent sub-factors: autonomy in language learning (β = 0.83, t = 17.14), environmental mastery (β = 0.72, t = 13.91), personal growth (β = 0.81, t = 16.74), positive relations with others (β = 0.77, t = 14.89), purpose in life (β = 0.75, t = 14.12), self-acceptance (β = 0.86, t = 17.52), listening (β = 0.63, t = 11.87), speaking (β = 0.70, t = 13.73), reading (β = 0.68, t = 12.56), and writing (β = 0.66, t = 12.08). Moreover, the results indicate that there is a significant positive association between LA and the following sub-factors: autonomy in language learning (β = 0.61, t = 11.12), environmental mastery (β = 0.48, t = 6.65), personal growth (β = 0.56, t = 10.32), positive relations with others (β = 0.52, t = 9.74), purpose in life (β = 0.50, t = 9.20), self-acceptance (β = 0.59, t = 10.68), listening (β = 0.38, t = 4.12), speaking (β = 0.41, t = 5.38), reading (β = 0.46, t = 6.31), and writing (β = 0.43, t = 5.72).

Fig. 3

The graphic representation of the path coefficients in Model 2

Fig. 4

Significance of path coefficient T-values for Model 2

Table 8 Highlights from Model 2’s outcomes

The gathered data gives a rich tapestry of insights on how the integration of such AI technologies is viewed and how they impact the academic growth of non-native English speakers. The examination of this data sheds insight into the multidimensional function of ChatGPT in aiding with language development and evaluation and gives a nuanced view of its influence on autonomy, AB, PWB, and learner achievement. Findings suggest that EFL students are more likely to succeed and less anxious during AI-integrated instruction when they demonstrate their competence and practice efficient learning methods. This is because they can display their true expertise, or this variety provides more entertainment in language development. Jin’s outcomes (2023) corroborated this result by pointing out that students were more likely to participate in online assessments when they exercised test-taking skills, AB, and autonomy. The study discovered that applying a useful app such as ChatGPT in instruction and evaluation helps enhance AB. In other words, AB is crucial for promoting good psychological attributes and a pleasant perception of the school setting.

However, there is not enough opportunity to explore the relationship between ICALA and its effects on AB due to the absence of identical studies on the topic. In a research, Lang et al. (2019) examined the influence of AB on social support in higher education. Their research outcomes demonstrated that both AB and social support enhanced the inspiration of university-level EFL students. Enthusiastic students are highly motivated to engage enthusiastically in outside-of-school pursuits. The results also suggest that enhancing learning by means of supporting activities among EFL students might be a strong predictor of AB. Positive psychology’s underpinning areas and specialties add validity to this result. This data implies that the advantages of AB among students in EFL are enhanced by the usage of AI apps. It can be implied that the more effectively learners engage in task-supporting language learning, the more advantageous improvements in educational perseverance, regularity of adaptation, personalized qualification, and willingness to embrace academic life will emerge.

Owing to this school of thought, competent learners should have the mental capacity and metacognitive skills required to persist through any learning situation while engaging in an active part of their educational opportunities. EFL students may be susceptible to experiencing stress or anxiety because of the challenges they may have in language acquisition via different AI apps. In such circumstances, AB will substantially aid them in keeping homeostasis and making well-considered decisions. LaSalle (2015) underlined the symbiotic link between AB, student participation, and academic confidence. As was previously noted, self-efficacy is a fundamental component of AB. Consequently, it is reasonable to argue that optimists have a higher probability of success in the business. Furthermore, Lee and Wong (2014) and Namaziandost et al. (2024) argued that giving students meaningful assessment remarks could assist them in enhancing their AB, mindsets, trait emotional intelligence, and engagement.

The current investigation presents options for additional research into the influence of ICALA on EFL students’ final achievement and academic integrity. This reality begs for formulating tactical approaches to incorporate AI into educational settings to help learners succeed without encouraging reliance. Guidelines should tackle the requirement for continual instruction in digital literacy for learners and educators to guarantee educational institutions can adjust to and profit from the growing AI environment (Utami et al., 2023; Zhuo et al., 2023). Authorities regarding schooling are recommended to evaluate students expressing their significant interest in AI technologies and their possible requirements for subsequent studies in academia (Namaziandost & Rezai, 2024b). In conclusion, this study contributes to the comprehension of the outcomes of using ICALA in educational environments, emphasizing the need to implement a well-rounded approach that promotes enhancing students’ academic skills in conjunction with the benefits provided by AI. It establishes a platform for ongoing study into successful techniques for introducing AI into educational practices, an environment that encourages technical competency and academic integrity. The findings and comments from this research underline the potential for AI to operate as a strong ally in education if its integration empowers students in their academic endeavors.

Conclusion

Incorporating an AI platform is desirable because it offers an exciting and captivating technological setting, especially in open and dispersed language learning. This will boost language learners’ capacity to successfully practice writing abilities, leading to a broader improvement of their competency in internal assessment and development of their writing output. The outcomes of this study reveal that alternatives and feedback supported by AI led to increases in EFL learners’ language abilities, desire to learn, fun in learning, AB, and PW. This shows that AI technology may aid learners in supervising and managing their school-related procedures. Such tools may assist in creating targets, monitoring successes, and making any required revisions. AI-driven instruction helps students take ownership of their learning experience and strengthen their oral communication abilities by giving tailored coaching and adaptive challenges that foster the advancement of metacognitive approaches.

A number of effective approaches may be used to execute ICAAL successfully. By using adaptive learning technologies that customize assessments according to the specific development of each student, it is possible to effectively cater to a wide range of requirements and learning preferences. This methodology fosters greater student involvement with the subject matter, therefore augmenting their independence. Furthermore, providing educators with proper training to use ICAAL technologies efficiently is crucial. Professional development seminars may provide instructors with the essential skills to smoothly incorporate these technologies into their teaching methods, guaranteeing that they optimize the advantages of ICAAL for their students.

Additionally, establishing collaborative platforms that enable students to exchange their learning experiences and resources may cultivate a feeling of community and assistance. Fostering peer contact and mutual motivation may improve both PW and academic achievement. These techniques enable educators to fully exploit the promise of ICAAL, resulting in enhanced language learning results and overall student well-being. This work establishes the foundation for future research and application in improving EFL teaching using novel evaluation techniques.

Notwithstanding the commendable aspects of the research, it suffers from some constraints enumerated below: the cultural milieu of Saudi Arabia is a determinative element that might constrain the investigation’s conclusions. The distinctive educational methods and cultural norms in Saudi Arabia may limit the generalizability of the findings to other geographic areas or educational systems, therefore restricting the wider implications of the study. Conducting comparative studies that include EFL learners from various cultural backgrounds might provide a more profound understanding of these processes. Moreover, the research may fail to include all potential confounding factors that might influence the connections among the target variables, such as socio-economic position, previous language competence, or levels of desire. The failure to consider this aspect may result in inadequate or deceptive findings on the interaction of the examined variables. Future studies should strive to include a more extensive range of factors to get a deeper understanding of their impacts.

Technological variability is also a contributing factor. Disparities in the availability and knowledge of advanced computer-assisted language evaluation techniques among participants may impact their academic achievement and mental health, complicating factors that have not been included in the study. A future potential survey might investigate the influence of different degrees of technology availability on the interrelationships among the observed factors. Furthermore, while using CFA and SEM provides strong analytical frameworks, these approaches are based on certain assumptions, such as normality and linearity. The violation of these assumptions can impact the outcomes and interpretations of the structural connections created in the research. Potential future investigations may include different analytical approaches or sensitivity assessments to assess the reliability of their results.