# Taming AI for education. Methods to integrate AI in the educational process

Hani Al-Ers, Aleksandra Malinowska, Mathis Mourey, Lampros Stergioulas

<figure><img src="image_1.png" /></figure>

## To cite this version:

Hani Al-Ers, Aleksandra Malinowska, Mathis Mourey, Lampros Stergioulas. Taming AI for education. Methods to integrate AI in the educational process. 2024. hal-04962738

HAL Id: hal-04962738

https://hal.science/hal-04962738v1

Preprint submitted on 19 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Taming AI for education. Methods to integrate AI in the educational process

Hani Al-Ersª, Aleksandra Malinowskaᶜ, Mathis Moureyª,¹, Lampros Stergioulasª

ªThe Hague University of Applied Sciences, The Hague, The Netherlands.
Corresponding author, mjfmourey@hhs.nl
ᵇUniv. Grenoble Alpes, Grenoble INP, CERAG, 38000 Grenoble
France

ᶜUniversity of California, Santa Barbara, CA, USA

## Abstract

This opinion paper provides a comprehensive framework for implementing AI project-based learning in universities. Our proposed AI project-based educational platform addresses current educational challenges by providing adaptive learning paths, personalized feedback, and a supportive, inclusive learning environment. The framework uses multiple specialized GPT models to teach, assess, guide, and help students through the module. Our proposition is supported by empirical evidence, gathered from experiments organized in a Dutch university and a secondary school, and by the existing literature. We also discuss the potential challenges of the approach.

**Keywords:** Education, Project-based, GPT, AI, Framework
# 1. Introduction

The advent of artificial intelligence (AI) in educational systems marks a significant turn from traditional pedagogical methods toward a future where learning is personalized, interactive, and deeply integrated with technology. In this paper, we provide a framework for AI project-based learning in universities. We present the advantages that AI can bring to education, and show an example framework for such a platform. Our argument is grounded in the understanding that AI has the potential to revolutionize how educational content is delivered, consumed, and assessed, offering both scalability and customization in learning experiences. However, despite promising developments, the integration of AI in education remains nascent and dispersed, lacking a cohesive strategy that could unlock its full potential to benefit learners and educators alike.

This paper aims to highlight the scattered yet innovative efforts in using AI within educational settings and argue for a unified approach that leverages AI to enhance learning outcomes. We propose that a holistic AI-based educational platform, particularly one employing a project-based learning approach, can address current educational challenges by providing adaptive learning paths, personalized feedback, and a supportive learning environment that is both inclusive and equitable.

The paper is organized as follows: After a literature review that maps the current landscape of AI applications in education, we present various benefits that an AI educational platform could offer to students, supported by empirical evidence. We then propose a novel system—a project-based learning approach enhanced by AI—detailing its design, functionality, and potential impact on the educational ecosystem. To support our proposal, we present the results of experiments where AI was integrated as an educational tool. The discussion section summarizes our proposal and discusses potential challenges that our approach would face.

# 2. Context

LLMs have the potential to revolutionize educational practices by automating tasks and personalizing learning experiences (Zawacki-Richter et al., 2019; Lo, 2023). The incorporation of LLMs into curriculums can facilitate a supportive educational environment, with teachers acting as facilitators in the transition towards more AI-integrated learning approaches. Studies by Clarizia et al. (2018); Belhaj et al. (2021); Tack and Piech (2022); Fauzi et al. (2023) have documented the multifaceted benefits of LLMs in enhancing student productivity, optimizing time efficiency, assisting with inquiries, and fostering collaboration among learners. These capabilities of LLMs underscore their potential to support a more dynamic and interactive educational environment, where information accessibility and learning efficiency are significantly improved.

This is not the first time we faced such a situation since some established technologies have already been successfully integrated into educational settings. Calculators, for instance, have been shown to enhance mathematical performance without undermining fundamental math skills (Boyle and Farreras, 2015), while the internet has enriched online learning practices significantly (Ghoshal and Upadhyay, 2023). However, the use of these technologies must be carefully managed; educators' strategic handling of calculators and the thoughtful limitation of internet access is crucial to minimize distractions and enhance learning outcomes (Laborde et al., 2006; Lo, 2023).
Additionally, student are already making significant use of GAI tools for their course work. Shoufan (2023) reports that approximately 96% of students expressed a keen interest in utilizing ChatGPT for their studies, with 83% inclined to use it more frequently for academic purposes. This high level of student engagement with ChatGPT indicates a paradigm shift in how educational content is accessed and interacted with, suggesting a potential reconfiguration of traditional learning environments towards more AI-integrated platforms. For K-12 education, the introduction of artificial intelligence (AI) presents a promising avenue for enhancing teaching and learning processes through improved planning, implementation, and assessment (Celik et al., 2022; Ji et al., 2023). The effectiveness of adopting AI-based chatbots in higher education is contingent upon addressing trust and ethical considerations. Research indicates that these factors significantly influence the successful implementation of chatbots in educational settings (Mohd Rahim et al., 2022).

The ethical implications of AI technologies in education have come to the forefront, especially in light of the increased use of online exam supervision technologies during the COVID-19 pandemic. This period unveiled a range of ethical concerns, from academic integrity to privacy and autonomy (Coghlan et al., 2021). Trust plays a pivotal role in the dynamics of educational settings, laying the foundation for effective teacher-student relationships and the successful integration of technological innovations. This underscores the importance of trust in traditional educational environments, a concept that extends to the domain of AI, where the introduction of tools like ChatGPT has raised concerns among educators (Alarcón Llontop et al., 2023; Harry, 2023).

Despite the growing interest in the application of ChatGPT and other LLMs in education, there is a call for more research to understand their efficacy and the ethical considerations involved (Adiguzel et al., 2023; Zhai, 2023; Tlili et al., 2023). The debate over the potential positive and negative impacts of ChatGPT on education, including concerns about diminishing human interaction and dependence on AI for basic skills (Zhai, 2023). This shows that the ideal framework for utilizing this technology and the role human educators will play within it is a core question. There is a growing interest in exploring the effectiveness of ChatGPT in teaching complex subjects, such as mathematics, to ascertain whether it can serve as a flexible tool capable of addressing the diverse educational needs of students (Adiguzel et al., 2023).

### 3. Educational Benefits for the students

Recent developments in AI are challenging existing views in education sciences. This technological advancement changed the existing paradigm, where, now, education is not just a one-size-fits-all endeavor but a deeply personalized journey that adapts to the unique needs, preferences, and learning styles of each student. Such personalizing is not merely a theoretical ideal but is grounded in the constructionist theories of learning, which emphasize the active role of learners in constructing knowledge through experiences and interactions, as highlighted by Vygotsky (1978).

Generative AI promotes active learning. Generative AI's capacity to tailor educational experiences finds its roots in adaptive learning systems that analyze learners' performance, learning styles, and preferences, thereby delivering customized content and learning pathways (Xie et al., 2019). This approach ensures that learners are not passive recipients of information but active participants in a learning process that is both relevant and engaging. Most current research states that the potential of Generative AI resides in personalizing.
However, Generative AI facilitates more than just personalizing, enabling the use of mastery-based progression and just-in-time education. Such methodologies ensure learners achieve a comprehensive understanding before progressing and acquiring knowledge exactly when needed, reflecting the shift towards more competency-based and efficient learning strategies (Guskey, 2010; Pane et al., 2017).

Furthermore, generative AI plays a crucial role in fostering intrinsic motivation among students, creating learning environments that promote deep engagement and the psychological state of flow, characterized by a sense of immersion and enjoyment in learning activities (Csikszentmihalyi, 1990).

Generative AI can provide nuanced and authentic evaluations. Traditional assessment methods often fail to capture the full spectrum of student abilities, particularly in complex cognitive and creative domains. AI-driven assessments provide personalized, adaptive feedback, offering deeper insights into students' understanding and skills, thereby aligning more closely with real-world applications of knowledge (Boud and Soler, 2016; Bennett, 2019). Furthermore, Generative AI has the advantage of being able to scale up with the number of students. Some departments or classes can provide high quality in their assessments due to their size, however, it becomes increasingly difficult to maintain such quality as the number of students increases.

Generative AI democratizes learning. By providing universally accessible learning opportunities, AI has the potential to bridge educational divides, ensuring that quality education is not a privilege of the few but a right for all, regardless of socio-economic background or geographical location (Lagemann, 2005; Kanaiyalal, 2022).

This paper proposes that an AI Project-based learning has the potential to become the most efficient pedagogical approach. Such a method would allow to provide high-quality education, putting students at the center of the learning process, while being able to scale up with ease. The next section presents a description of such a method.

## 4. AI Project-based Learning

This section presents a structure for an AI Project-Based learning platform. The platform, in essence, proposes to automatize time-intensive tasks that, traditionally, staff have managed, and to offer classes with personalized feedback for all students. It leverages the advantages that Generative AI can bring to education as described in section 3. The platform is organized as depicted in Figure 1.

The framework proposed is composed of three blocks:

* **Student intake**: New students are first met with an intake interview run by a pre-trained GPT model, specialized in intake interviews, and prompted to follow a strict interview guide. The Intake GPT will assess the strengths and weaknesses of students and will determine if the student should follow the AI Project-based course. The log of the intake interview as well as the advice of the IntakeGPT will then be shared with the ProfilerGPT.

The ProfilerGPT produces a student profile that will guide the teaching AI to provide tailor-made feedback to the student. The Administrative staff has to review the log of the intake interview and the student profile. Based on his assessment of these documents, he gives his approval for the student to actually take part in the course.
<figure><img src="image_3.png" /><figcaption>Figure 1: AI Project-based Learning</figcaption></figure>

*   **Project:** The student starts the course with a project description, that details all the specifics of the course, and the full corpus of class materials. The project is self-driven in the sense that the student is completely responsible for their own learning process. They can interact with the teaching AI (TeacherGPT) via a chat.

    The TeacherGPT is pre-trained on the project description, all necessary course materials but also on the student profile so that it can adjust specifically to student needs. The student is asked periodically to submit a progress report to the TeacherGPT. The log of the chat with TeacherGPT is continuously shared on a Dashboard. The dashboard provides information about the student's progress on the project but also can report potential failures/issues encountered in the learning process.

*   **Quality control and supervision:** When the student is ready to submit the project deliverables, they can submit it to the TeacherGPT. The TeacherGPT reviews the work and provides feedback to the students for them to improve on and shares their insights on the dashboard. The dashboard combines information from the chat log that allows him to provide an estimation of the course progress but also information from the TeacherGPT.

    The chat log and assessment of TeacherGPT goes through the QualityControlGPT that will make a preliminary check for potential issues in the learning process and will evaluate the quality of the assessment of TeacherGPT. His assessment will be forward to the dashboard for the staff member to review. The assessment from QualityControlGPT will also be shared with TeacherGPT to allow for continuous improvement.

    All this information is condensed into a dashboard available to a staff member. The staff is responsible for analyzing the dashboard focusing on specific errors or mistakes
and has to approve the assessment of the TeacherGPT. Once the student has their work accepted by the staff member, they are allowed to continue to the next stage of the module or to the next module. We could imagine, for instance, that the student can move on to the next project once the assessment has been completed by the staff member.

# 5. Experiments with AI Project-based Learning

This section synthesizes empirical data from various studies to evaluate the effectiveness of generative AI in different aspects of the teaching process. These studies investigate how AI can enhance student engagement, instructional efficacy, and assessment accuracy. Additionally, they explore design principles critical for developing effective, ethical, and trustworthy AI-integrated educational platforms. The insights gained from these studies help inform best practices for the implementation and optimization of AI in educational settings.

## 5.1. *Trust in AI Teaching Tools*

A fundamental foundation for the success of such a (semi) automated educational system is the acceptance of the learners of this new paradigm. In a study conducted at The Hague University of Applied Sciences, students were surveyed to determine which design principles most influence their trust in AI-powered teaching tools. A number of principles were identified in the literature. The principles evaluated were privacy, intelligence, fairness, controllability, engagement, transparency, and friendliness (for Education Youth Sport and Culture, 2022; Guo et al., 2022; IBM, 2022; Google, 2023; of Education, 2023). Results from thirty-nine participants indicated a general importance placed on all principles, with "friendliness" consistently rated as the least important. This suggests that while students value a positive and amicable interaction with AI, other functional attributes are deemed more crucial for trust.

When examining the data across different demographics (Peney et al., 2024), clear patterns emerged that can inform the design of AI-integrated educational platforms. Female students prioritized "fairness," "friendliness," and "engagement," indicating that these aspects should be emphasized to create a supportive and inclusive learning environment. Students enrolled in technical study programs showed a higher concern for "privacy," reflecting their acute awareness of data security issues. Non-technically savvy students placed greater emphasis on "fairness," suggesting a broader concern for equitable educational experiences.

Based on these findings, the following design principles are recommended for AI educational platforms:

1. **Prioritize Privacy and Security:** Given the heightened concern for privacy among some students, robust data protection measures must be integrated into the platform. This includes secure data storage, minimal data usage, and transparent data handling policies.

2. **Ensure Fairness and Transparency:** To build trust, especially among female students and non-technical students, the platform should prioritize fairness in its algorithms and be transparent about how decisions are made. This can be achieved by providing clear explanations of AI processes and ensuring that the AI system treats all students equitably.

3. **Incorporate Customizable Controls:** Allowing students to control certain aspects of their interaction with the AI can improve their sense of autonomy and trust. Customizable features can help cater to diverse preferences and learning styles.
4. **Foster a Supportive Learning Environment:** To address the needs of students who value engagement and fairness, the platform should include features that promote collaboration and provide personalized support, such as AI-driven feedback and adaptive learning pathways.

5. **Enhance Engagement and Friendliness:** While friendliness was rated the least important, creating an engaging and user-friendly interface remains crucial for overall user satisfaction. Features that promote in

## 5.2. *Teaching math in secondary school*

We questioned whether the AI of ChatGPT could take on the role of an instructor to support students in learning a new topic. To examine this we conducted a study that looked into the applicability of ChatGPT for teaching trigonometry to secondary school students, this study examined the model's efficacy in comparison with conventional teaching methods. Students were divided into two groups: one assisted by ChatGPT and the other instructed by a teacher. Post-lesson assessment revealed no clear difference in performance between the groups.

Surprisingly, observations showed the formation of student groups in the ChatGPT-assisted environment. Despite interacting with AI, these students sought collaborative learning, a hallmark of traditional classroom dynamics. They discussed answers and shared tips on using AI effectively. The study also highlighted some challenges, such as the students' struggle to optimally utilize ChatGPT, pointing towards a need for enhancing critical thinking skills to leverage AI tools effectively.

## 5.3. *Grading open questions using ChatGPT*

The proposed architecture also contains an assessorGPT that can grade the student's progress. Automating the grading of closed questions (such as multiple choice) is a trivial matter, however one can wonder whether this is also possible for open questions. In previous work by the authors (Alers et al., 2024), ChatGPT-4 was tested for its capability to grade open-question exams at the Hague University of Applied Sciences. Despite some disparities, a Pearson correlation test showed that there was a positive correlation between the grades awarded by human lecturers and ChatGPT-4 with *r* = 0.878, *n* = 105, *p* < 0.001. This shows that AI could mirror human grading patterns to a significant extent.

It took ChatGPT-4 around a minute to grade each exam, which is a fraction of the time it took human graders. Additionally, ChatGPT-4 provided individualized feedback that was not only specific but also clear to the students, facilitating their understanding and learning. This is something human lecturers did not do due to lack of time during grading. This finding supports the potential of AI systems like ChatGPT-4 in assisting with educational assessment, though careful consideration must be given to its implementation.

# 6. Conclusion

This paper proposes the use of AI project-based learning in universities. The previous sections detailed the functioning of such an approach and provided a few examples of how the approach could benefit existing educational institutions. The advantages are clear, however, several challenges demand attention to ensure the responsible and equitable use of AI-based approaches.
Data privacy and security emerge as paramount concerns, given the sensitivity of stu-
dent data and the potential risks of breaches, which can compromise the integrity and trust
in educational systems. As Kamenskih (2022) notes, the educational sector's increasing
reliance on digital technologies amplifies the vulnerability to cyber-attacks, underscoring
the need for robust data protection measures and ethical AI design that prioritizes minimal
data usage (Vannini et al., 2019).

Another critical issue is the inherent bias and inequality that AI systems can perpetuate. Given that AI algorithms are only as unbiased as the data they are trained on, there's a significant risk of reinforcing societal disparities. This challenge calls for a concerted effort to diversify training data and ensure AI algorithms are audited for bias, as suggested by research on conservative AI and social inequality. Addressing these biases is crucial for fostering an educational environment that upholds the principles of fairness and equality for all students, irrespective of their background.

The quality and accuracy of AI-generated content in education also present significant challenges. The reliance on AI to generate educational materials necessitates stringent quality controls to prevent misinformation and ensure alignment with curricular standards (AIGC, 2023). Moreover, the increasing dependence on technology in education raises concerns about the erosion of critical thinking and interpersonal skills, suggesting a need for a balanced approach to technology integration that preserves human interaction and pedagogical richness (Ben-Jacob and Glazerman, 2021).

Because of Generative AI's limitations, students must possess solid background knowl-
edge to discern the accuracy of outputs from LLMs. Shoufan (2023) and Jalil et al. (2023)
highlight the necessity for a critical evaluation of the information provided by LLMs, point-
ing out that despite their capabilities, these models cannot wholly substitute human intel-
ligence. Additionally, the adoption of such technologies is accompanied by risks, including
plagiarism, an over-reliance on LLMs by students, and ethical concerns related to privacy,
bias, and transparency (Zawacki-Richter et al., 2019; Addington, 2023; Zhai, 2023).

Collectively, these challenges underscore the complexity of integrating AI in educational
settings. As the financial saying goes “there is no free lunch”. It is up to the educational
institutions to decide whether the benefits are worth the risks. It is often easy to refute
new technological solutions on the basis of the risks they offer. But we tend to forget that
our current educational model is full of flaws and risks that we grew to accept over time.
Given time, we might grow to accept the risks of AI technologies in education too.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References

Addington, S. (2023). Chatgpt: Cyber security threats and countermeasures. SSRN.

Adiguzel, T., Kaya, M. H., and Cansu, F. K. (2023). Revolutionizing education with ai: Exploring the transformative potential of chatgpt. *Contemporary Educational Technology, 15*(3):ep429.

AIGC (2023). A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. *arXiv preprint arXiv:2303.04226*.

Alarcón Llontop, L. R., Pasapera Ramírez, S., and Torres-Mirez, K. (2023). The chatgpt application: Initial perceptions of university teachers. In *Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development (Paper 336)*, Buenos Aires. LACCEI.

Alers, H., Malinowska, A., Meghoe, G., and Apfel, E. (2024). Using chatgpt-4 to grade open question exams. In *Future of Information and Communication Conference*, pages 1–9, Cham. Springer Nature Switzerland.

Belhaj, N., Hamdane, A., Chaoui, N. E. H., Chaoui, H., and Bekkali, M. E. (2021). Engaging students to fill surveys using chatbots: University case study. *Indonesian Journal of Electrical Engineering and Computer Science*, 24(1):473.

Ben-Jacob, M. and Glazerman, A. (2021). The ethical implications of the overuse of technology in education. *Journal of Business Ethics*, 170(4):695–707.

Bennett, R. E. (2019). The changing nature of educational assessment. *Review of Research in Education*, 43(1):370–393.

Boud, D. and Soler, R. (2016). Sustainable assessment revisited. *Assessment & Evaluation in Higher Education*, 41(3):400–413.

Boyle, R. W. and Farreras, I. G. (2015). The effect of calculator use on college students’ mathematical performance. *International Journal of Research in Education and Science*.

Celik, I., Dindar, M., Muukkonen, H., and Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. *TechTrends*, 66(4):616–630.

Clarizia, F., Colace, F., Lombardi, M., Pascale, F., and Santaniello, D. (2018). Chatbot: An education support system for student. In *Cyberspace Safety and Security: 10th International Symposium, CSS 2018, Amalfi, Italy, October 29–31, 2018, Proceedings 10*, pages 291–302. Springer International Publishing.

Coghlan, S., Miller, T., and Paterson, J. (2021). Good proctor or “big brother”? ethics of online exam supervision technologies. *Philosophy and Technology*, 34(4):1581–1606.

Csikszentmihalyi, M. (1990). *Flow: The psychology of optimal experience*. Harper & Row.

Fauzi, F., Tuhuteru, L., Sampe, F., Ausat, A. M. A., and Hatta, H. R. (2023). Analysing the role of chatgpt in improving student productivity in higher education. *Journal on Education*, 5(4):14886–14891.

for Education Youth Sport, E. C. D.-G. and Culture (2022). *Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators*. Publications Office of the European Union.
Ghoshal, S. and Upadhyay, A. (2023). The effect of internet on students studies: A review. *EPRA International Journal of Multidisciplinary Research*, 9(7).

Google (2023). Responsibility: Our principles.

Guo, Y., Wang, J., Wu, R., Li, Z., and Sun, L. (2022). Designing for trust: A set of design principles to increase trust in chatbot. *CCF Transactions on Pervasive Computing and Interaction*, 4(4):474–481.

Guskey, T. R. (2010). *Mastery learning*, volume 2, pages 527–531. SAGE Publications, Inc.

Harry, A. (2023). Role of ai in education.

IBM (2022). Building trust in ai.

Jalil, S., Rafi, S., LaToza, T. D., Moran, K., and Lam, W. (2023). Chatgpt and software testing education: Promises & perils. In *2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)*, pages 4130–4137.

Ji, H., Han, I., and Ko, Y. (2023). A systematic review of conversational ai in language education: focusing on the collaboration with human teachers. *Journal of Research on Technology in Education*, 55(1):48–63.

Kamenskih, M. (2022). The analysis of security and privacy risks in smart education environments. *Journal of Open Source Computer and Software*, 4(2).

Kanaiyalal, T. (2022). Reshaping learning and teaching. ensuring equal accessibility, affordability and multi-disciplinary approach in higher education through technology. Technical report, Graduate Research in Engineering and Technology.

Laborde, C., Kynigos, C., Hollebrands, K., and Strässer, R. (2006). *Teaching and Learning Geometry with Technology*, page 275–304. BRILL eBooks.

Lagemann, E. C. (2005). The elementary and secondary education act at 40: Equity, accountability, and the evolving federal role in public education. *Review of Research in Education*, 29:51–67.

Lo, C. K. (2023). What is the impact of chatgpt on education? a rapid review of the literature. *Education Sciences*, 13(4):410.

Mohd Rahim, N. I., Iahad, N. A., Yusof, A. F., and Al-Sharafi, M. A. (2022). Ai-based chatbots adoption model for higher-education institutions: A hybrid pls-sem-neural network modelling approach. *Sustainability*, 14(19):12726.

of Education, U. D. (2023). Artificial intelligence and future of teaching and learning: Insights and recommendations. Technical report, U.S. Department of Education, Office of Educational Technology, Washington, DC.

Pane, J. F., Steiner, E. D., Baird, M. D., and Hamilton, L. S. (2017). Informing progress: Insights on personalized learning implementation and effects. Technical report, RAND Corporation.

Peney, L., Dernee, R., and Alers, H. (2024). Encouraging trust in ai-powered teaching tools: Ranking design principles. In *IEEE Conference on Artificial Intelligence (IEEE CAI 2024)*.

Shoufan, A. (2023). Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. *IEEE Access*, 11:38805–38818.
Tack, A. and Piech, C. (2022). The ai teacher test: Measuring the pedagogical ability of blender and gpt-3 in educational dialogues.

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., and Agyemang, B. (2023). What if the devil is my guardian angel: Chatgpt is a case study of using chatbots in education. *Smart Learning Environments*, 10(1).

Vannini, S., Gasser, U., and Nanni, M. (2019). “mind the five”: Guidelines for data privacy and security in humanitarian work with undocumented migrants and other vulnerable populations. *Journal of the Association for Information Science and Technology*, 71(1):5–20.

Vygotsky, L. (1978). *Mind in society: The development of higher psychological processes*. Harvard University Press.

Xie, H., Chu, H. C., Hwang, G. J., and Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/ personalized learning: A systematic review of journal publications from 2007 to 2017. *Computers & Education*, 140:103599.

Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? *International Journal of Educational Technology in Higher Education*, 16:Article 39.

Zhai, X. (2023). Chatgpt and ai: The game changer for education.