Impact of AI-generated curricula based on industry needs on students' motivation

Artificial intelligence (AI) has increasingly permeated higher education, offering innovative solutions for curriculum development that align with rapidly changing job market demands. The advent of AI-generated curricula presents opportunities to personalize learning experiences, potentially enhancing student motivation and engagement through tailored content and adaptive learning pathways. As the labor market evolves, there is a pressing need for educational institutions to equip students with relevant skills, and AI can play a pivotal role in bridging this gap.

Despite these technological advancements, there is a notable paucity of research examining the psychological impacts of AI-generated curricula on students. Existing studies predominantly focus on the technical implementation and efficacy of AI tools, often overlooking how these innovations affect student motivation and engagement from a psychological standpoint. Specifically, the influence of AI-generated curricula on intrinsic and ex- trinsic motivation as outlined in selfdetermination theory and the facilitation of flow states in learning environments remain underexplored. Addressing this gap is crucial, as motivation and engagement are key determinants of academic success and lifelong learning.

This study aims to assess student perceptions and psychological impacts of AI-generated curricula in higher education. The primary research question is: “How does the use of AI-generated curricula affect student motivation and engagement in higher education?” We hypothesize that AI-generated curricula, by personalizing learning experiences based on job market needs, enhance intrinsic motivation and foster higher levels of engagement among students. By integrating psychological theories of motivation with pedagogical strategies and technological advancements, this research seeks to provide a comprehensive understanding of the interplay between AI-generated curricula and student engagement.

Understanding these dynamics not only fills a critical gap in the literature but also offers practical insights for educators and policymakers. Such insights are essential for developing effective pedagogical approaches that promote active learning and sustained student engagement while meeting the evolving requirements of the labor market.

Literature review

In recent years, there has been increasing recognition of the disconnect between academic education and the skills required in professional environments. Tenhunen et al. [1] emphasize this gap, demonstrating through the Software Development Academy that hands-on, real-world projects better prepare students for employment. This underscores the need for curricula that not only impart theoretical knowledge but also integrate practical, job-relevant skills.

To address this gap, some researchers have proposed leveraging artificial intelligence (AI) to enhance curriculum development. To address this gap, some researchers have proposed leveraging artificial intelligence (AI) to enhance curriculum development. Hassan et al. [2] propose a data-driven approach that analyzes job postings and candidates' resumes to align IT-related courses with industry-required skills. This illustrates how technology can complement traditional experiential learning methods by ensuring that educational programs remain in sync with real-time labor market demands.

Similarly, Padovano and Cardamone [3] explore AI's potential in curriculum design, particularly within industrial engineering and management. Their work focuses on identifying educational gaps and using AI-driven techniques to develop competency-based curricula aligned with future industry trends. This approach suggests that AI can support more dynamic, forward-looking curriculum design across different fields, expanding beyond the immediate demands of the labor market.

However, while efforts to align curriculum with industry needs are growing, there remains a notable lack of focus on the impact of these changes on student motivation. Bopp et al. [4] address this by identifying components that affect students' motivation in competency-based curricula. They find that factors such as structured learning milestones, enthusiastic faculty, and the relevance of content to real-world practice are key to maintaining student engagement. This highlights the importance of designing curricula that not only meet industry needs but also foster intrinsic motivation, an essential factor in student success.

Methodology

This study will employ a quantitative approach to assess student perceptions and the psychological impacts of AI-generated curricula. The focus will be on understanding how AI-based curricula influence student motivation, engagement, and overall learning experiences, with insights drawn from quantitative data collected via Google Forms.

The participants will be undergraduate and postgraduate students from various disciplines at higher education institutions where students are suggested to be familiar with AI-enhanced educational tools. Participants will be selected through convenience sampling by distributing the survey via student mailing lists, social media platforms, and educational forums. A sample size of 100-150 students is targeted to provide diverse perspectives.

The data collection tool will be a structured Google Forms questionnaire with several sections. It will gather demographic information (age, gender, field of study, academic level), assess motivation and engagement (intrinsic and extrinsic), explore psychological impacts (motivation, satisfaction), and gauge perceptions of AI-generated curricula regarding personalization and job market relevance. Additionally, open-ended questions will provide qualitative insights into students' views on AI-driven educational tools.

The survey will include Likert scale questions to quantify student perceptions, such as motivation and curriculum relevance to the job market. Multiple-choice questions will explore specific aspects like personalization and flexibility, while open-ended questions will allow participants to provide detailed feedback on how AI-generated curricula have impacted their learning experience. This combination ensures both quantitative and qualitative data collection for a comprehensive analysis.

The study examines the impact of AI-generated curricula compared to traditional curricula, focusing on several key dependent variables: student motivation, career preparedness, curriculum relevance, and engagement. Motivation is assessed through both intrinsic (personal interest) and extrinsic (career goals) factors. Career preparedness measures students' confidence in their curriculum's ability to equip them with relevant job market skills. Curriculum relevance evaluates the perceived alignment between the academic content and industry demands. Engagement is examined using flow theory, which looks at how deeply students are immersed in their learning activities. These variables are used to understand how AI-generated curricula might enhance students' academic experiences and psychological responses

Quantitative data will be analyzed using descriptive statistics, including measures like mean, standard deviation, to examine relationships between variables such as motivation, engagement, and perceived job relevance. Ethical considerations include informed consent, confidentiality, and participant anonymity. Limitations include potential self-reporting bias and sample representative- ness, affecting the generalizability of findings.

This study will prioritize informed consent, anonymity, and confidentiality. Participants will be informed of the study's purpose, voluntary nature, and privacy through a consent form included in the Google Form survey. Responses will be anonymized, with no personal identifying information collected, ensuring the data is used solely for research purposes while protecting participant privacy.

This research has a few limitations. First, self-reporting bias may affect the accuracy of the data, as participants may overestimate or underestimate their motivation, engagement, or psychological impacts. Second, the use of convenience sampling limits the representativeness of the sample, which consists of students from Kazakhstan. This may affect the generalizability of the findings to the broader student population, especially those unfamiliar with AI-generated curricula or studying in less AI-impacted fields.

Results

The study aimed to assess the impact of integrating AI-generated curricula aligned with job market needs on students’ motivation in higher education. Survey data reveals varying levels of student satisfaction, confidence, and motivation based on their academic level, as shown in Table.

Table. Cross-Tabulation of Student Perceptions by Degree Level Regarding Curriculum Alignment, Confidence, Motivation, and Satisfaction

Cross tabulation		What degree are you currently pursuing?
		Bachelor’s degree	Master’s degree	Doctoral degree
To what extent does the curriculum allow you to choose topics that align with your personal career goals? (1 - Not at all, 5 - A great deal)	Mean	2.86	2.81	3.69
	Median	3	3	4
How confident do you feel that the skills taught in your program will prepare you for the job market? (Not confident at all, 5 - Very confident)	Mean	2.78	2.73	3
	Median	2	3	3
How much do you feel that the curriculum reflects current industry needs and trends? (1 - Not at all, 5 -A great deal)	Mean	2.83	2.75	3
	Median	2	2.5	3
To what extent does the curriculum motivate you to learn beyond what is required for assessments? (1 -Not at all, 5 - A great deal)	Mean	2.76	2.67	3.46
	Median	2	2.5	4
How much does the potential for future employment motivate you to engage with the curriculum? (1 - Not at all, 5 - A great deal)	Mean	2.84	2.85	3.46
	Median	2	3	4
How satisfied are you with the alignment between the curriculum and job market expectations? (1 - Very dissatisfied, 5 - Very satisfied)	Mean	2.81	2.75	3.38
	Median	2	3	3

Doctoral students reported higher satisfaction with the alignment between their curriculum and personal career goals, with a mean score of 3.69, compared to 2.86 for bachelor's and 2.81 for master's students. This suggests that more advanced students perceive their curricula as more tailored to their career trajectories, potentially due to the specialized nature of doctoral programs.

Similarly, confidence in career preparedness was slightly higher among doctoral students (mean = 3) compared to bachelor's (mean = 2.78) and master's (mean = 2.73) students. However, all groups reported moderate levels of confidence, indicating a general uncertainty about whether their curricula equip them with the necessary skills for the job market.

When asked about the relevance of the curriculum to current industry needs, doctoral students again reported a higher mean score (3), while bachelor's (2.83) and master's (2.75) students expressed less confidence in the market alignment of their programs. This suggests a perceived gap between academic content and industry demands, particularly at the undergraduate and master’s levels.

In terms of motivation, doctoral students exhibited a stronger drive to engage with the curriculum beyond what is required for assessments, with a mean score of 3.46 compared to 2.76 for bachelor's and 2.67 for master's students. Similarly, the potential for future employment appeared to be a greater motivating factor for doctoral students (mean = 3.46) compared to bachelor's (2.84) and master's (2.85) students. This highlights the importance of career alignment in maintaining student engagement.

Overall, the results suggest that students at the bachelor's and master's levels are not fully satisfied with the alignment between their current curricula and job market needs. This dissatisfaction indicates a desire for more market-oriented, personalized learning pathways, aligning with existing literature that calls for curricula integrating practical, industry-relevant skills.

Discussion

The findings, framed by SelfDetermination Theory (SDT), highlight how AI-generated curricula could enhance student motivation and engagement by addressing key psychological needs: autonomy, competence, and relatedness.

Autonomy refers to the sense of control students feel over their learning. Doctoral students reported greater alignment between their studies and career goals compared to bachelor's and master's students, suggesting a lack of autonomy at lower academic levels. AI-generated curricula can address this by personalizing learning paths, allowing students to align their studies more closely with personal career aspirations, which could increase intrinsic motivation [5].

Students across all levels expressed moderate confidence in their curricula’s ability to prepare them for the job market, with doctoral students feeling slightly more prepared. AI-generated curricula, which continuously adapt to real-time industry needs, could enhance students’ sense of competence by providing up-to-date, relevant skills. This alignment with labor market trends can boost both intrinsic and extrinsic motivation, helping students feel more capable and confident. As Khodyreva et al. [6] argue, aligning educational programs with regional labor market demands ensures that students develop jobready competencies, further supporting their motivation.

The sense of relatedness, or connection to future careers, was stronger among doctoral students, who perceived their curricula as more aligned with industry needs, while bachelor's and master's students experienced a disconnect between academic content and job market demands. AI-generated curricula, designed to reflect real-time labor trends, could improve this connection, enhancing both extrinsic motivation through clearer career paths and intrinsic motivation by making learning more relevant. This alignment is essential for fostering student engagement and satisfaction. A similar challenge is observed in Kazakhstan, where a study by Tleuzhanova et al. [7] found a mismatch between the skills provided by higher education institutions and employer expectations, despite high graduate employment rates. Employers emphasized the need for better training quality to ensure that young professionals are more competitive in the labor market. Aligning curricula with industry needs, as AI-generated curricula can, is key to addressing these gaps and preparing students for successful employment.

AI-generated curricula can enhance motivation by providing personalized, careerrelevant experiences. According to SDT, aligning learning with personal and professional goals fosters intrinsic motivation. Additionally, AI can promote autonomy and engagement through interactive tools that adapt to individual progress, creating optimal challenges and promoting deep engagement in learning.

The dissatisfaction of students, particularly at the bachelor’s and master’s levels, with their current curricula suggests a need for more personalized, market-oriented programs. AI-generated curricula offer a promising solution by addressing students' needs for autonomy, competence, and relatedness, ultimately enhancing both intrinsic and extrinsic motivation. Thoughtful implementation will be key to their success in fostering engagement and preparing students for future careers.

Conclusion

AI-generated curricula might turn out to be the very thing that has so far been missing in higher education in terms of aligning academic programs with the demands of the job market, hence finally yielding increased engagement and motivation among students. Be- cause of the personalization and flexibility, such curricula would be touching on those significant psychological needs that SelfDetermination Theory identifies as major motivators in students: autonomy, competence, and relatedness. While this deficit in satisfaction may be regularly found among undergraduate and master's students, the fit between studies and career goals is already better for specific examples among doctoral candidates. An AI-designed curriculum has the potential to bridge the relevance gap by offering customized learning experiences that are more industry-oriented, which will spur intrinsic and extrinsic motivation. These could, therefore, be instituted within the practice of educators and policymakers by furnishing them with strategies that, while responding to industrial needs through adapting the content, would also engage the students in active and engaged learning. However, such initiatives will surely succeed only when thoughtful implementation balances technological advancement with the psychological and pedagogical needs of the learners themselves. All these, among other approaches, indeed call for further research in refining the long-term impact such that the AI-driven curriculum best serves its purpose of bringing forth academically and professionally successful students.