Transforming group work: the impact of generative AI integration in an introductory research methods course Open Access

Group work, working in small groups or teams, stems from the cooperative learning paradigm (Slavin, 2010) and the collaborative learning paradigm (Dillenbourg, 1999). It emphasizes learning together to develop a shared understanding while maintaining shared responsibility and individual accountability in achieving common goals. Group work is essential to develop learning outcomes at the program level in higher education, such as problem-solving, making judgments, effective communication, independence and responsibility. These outcomes cannot be developed appropriately and sufficiently by traditional lectures, individual examinations or coursework (e.g. assignments or presentations).

Group work has been a widely used didactic method since the 1980s (Yang, 2023) and has become prevalent since 2010 based on Web of Science search results (Clarivate, 2025). This trend reflects the growing number of students resulting from the internationalization of Dutch higher education between 2000 and 2015 (Ybema and Bormans, 2024). Group work can be seen as a human expression of co-intelligence or as co-intelligence facilitated by educational technologies, such as computer-assisted collaborative learning (CSCL) or collaborative knowledge building (CKB) (Stahl, 2006). How group cognition is mediated and developed in digital learning environments has been the major research focus of CSCL and CKB. With the rise of generative artificial intelligence (GenAI henceforth), co-intelligence increasingly shifts towards human-machine collaboration (Mollick, 2024). As a result, group work is evolving into a pluralistic model of human-machine collaboration.

GenAI tools have the potential to support learner-content interaction by acting as dialogic partners in the thinking process (e.g. Wise et al., 2024), which in turn promotes collaborative learning (Msambwa et al., 2025). Although GenAI-driven collaborative platforms enhance learning by incorporating game-like elements and providing instant feedback (Kovari, 2025), their prompt-based content generation differs significantly from the design of CSCL and CKB environments. These environments emphasize structured scripting and prompting strategies that engage students in knowledge sharing, critical discussions, decision-making and co-construction of knowledge.

Designing effective group work in the age of GenAI is inherently complex and presents three challenges in our context. The primary challenge lies in achieving constructive alignment among the various components of educational design. For example, one comprehensive framework available to guide teachers in this process is the Group Learning Activities Instructional Design framework (de Hei et al., 2020). This framework identifies eight essential components for designing group learning activities: (1) interaction, (2) learning objectives and outcomes, (3) assessment, (4) task characteristics, (5) structuring, (6) guidance, (7) group constellation and (8) facilities. Its primary aim is to help educators ensure alignment among these components to enhance the likelihood that collaborative learning leads to the desired educational outcomes. Among these components, assessment requires particular attention. When students lack adequate collaboration skills, assigning graded group work may result in unfair evaluation practices (Meijer et al., 2020).

The second challenge is the current impracticality for individual teachers to develop internal groupware using GenAI to support student collaboration. The General Data Protection Regulation (GDPR, 2016) imposes strict data protection requirements. As a result, mandating the use of external GenAI-based platforms such as Team-GPT (https://team-gpt.com/), especially those not officially endorsed by the university, for compulsory group assignments becomes highly problematic.

The third challenge involves integrating GenAI tools into assessment. According to the European Union (EU) Artificial Intelligence (AI) Act (i.e. Article 4, European Union, 2024), such integration requires that students first attain a sufficient level of AI literacy. Without this foundational literacy, the effective and equitable use of GenAI in assessments cannot be ensured.

The integration of GenAI in education has emerged as a central focus in educational discourse and practice over the past several years. There are many discussions among educators and researchers, varying from whether students should be allowed to use GenAI in their assignments to how educators can make sure student assignments are GenAI-free. There are also a few studies on how GenAI can go along with skills such as critical thinking (e.g. Hadi Mogavi et al., 2024) and group work (e.g. Zercher et al., 2025). The systematic review of Tan et al. (Tan et al., 2022) about AI techniques in collaborative learning reveals that AI is used for various purposes such as assessing learning outcomes and student discussions and supporting collaborative learning, yet empirical studies are lacking. Anderson et al. (Anderson et al., 2025) present a number of GenAI activities that can foster collaborative learning, including supporting group discussions using the prompts of LLMs, incorporating AI-embedded units into the curriculum and designing chatbots. Perifanou and Economides (2025) investigate how postgraduate students use GenAI in teamwork. This empirical study reveals that while teams appreciate some of the features of such technologies, such as the structure and the speed of responses, they still question the accuracy and consistency of their output.

Innovations in education, such as the integration of GenAI, may shift the dynamics in group work, making the accomplishment of assignments more fruitful or challenging. When students are new to group work and their study program and do not have knowledge and skills on how to use GenAI appropriately and responsibly, it becomes crucial for educators to make necessary adjustments in their teaching activities. The current study contributes to existing studies on the role of GenAI in collaborative work while also filling the gap for empirical research on how such technologies are used in practice to design a course and how students experience it. Our study highlights three challenges of designing effective group work, especially in contexts where teachers receive limited technological support from their institutions.

The purpose of this study is to redesign an existing research skills course and integrate AI literacy and collaborative skills into formative group assignments. It also investigates how students approach the use of GenAI in research design in group assignments. The key objectives are to help students improve their ability to critically and responsibly use AI in research, strengthen collaborative learning and explore students’ strategies for applying GenAI in research design. The following research questions are addressed:

1. How are course learning goals adapted to incorporate GenAI tools?

2. Which didactic approaches (e.g. flipped teaching and blended learning) are used to replace or supplement the existing course design?

3. What assessment strategies are used to replace or supplement the existing course design?

To answer RQ1, we collected and analyzed course team discussion notes and course documents (i.e. syllabus, lesson plans, assessment instructions and rubrics).

To answer RQ2, during in-class group activities, we observed how GenAI tools were used in group work, asked students to reflect on their own role, the roles of other members and the role of the GenAI, using a mini-survey (Forsyth, 2019; Wood and Moss, 2024), and held three focus groups on how the GenAI impacted their development of essential skills.

A mixed-method approach was adopted in this study for two main reasons. First, as our research aimed to redesign a course as well as investigating students’ experiences, mixed-methods allowed us to address different research questions adequately. Second, collecting data from students via mini-surveys, observations and focus groups at different stages of the course enabled us to capture a comprehensive picture of the experiences efficiently and effectively.

This study is situated in the context of the first semester of a bachelor’s program in the academic year of 2024–2025. The first author is the coordinator and one of the lecturers in the course.

Doing Research: Methodology is a research course offered in the bachelor’s program of Digital Culture and Society at Tilburg University in the Netherlands. Tilburg University uses a constructive alignment model to ensure learning goals, didactive appoaches and assessment align with each other and a blended learning approach to establish a learning environment for students using various delivery modes, learning formats and technological integration. It requires that learning goals pertain to knowledge, skills and character both at the program level and at the course level (Tilburg University, 2017). Against this background, this course aims to have students gain knowledge and understanding of the main research paradigms, the empirical cycle, responsible research and a range of different research methods (knowledge), perform and report specific steps of the empirical cycle (skills), acknowledge their own intuitions and prejudices and take a critical stance towards the normative aspects of research methods and research paradigms (character). During the course, students complete a variety of formative and summative assignments, individually or as a group. At the end of the course, students take a final exam and submit a written assignment individually. Students pass the assignments with a minimum grade of 6 on a scale of 10.

In the academic year of 2023–2024, before redesigning the course, the assessments consisted of a portfolio of seven small assignments (70%) and a final exam (30%). Initially, there was no statement in the syllabus about the use of GenAI tools. Throughout the semester, in line with the university-wide AI policy developments, students were informed that GenAI could be used in limited ways, such as for style editing and language checking. However, the lecturers encountered that students frequently referred to GenAI tools to complete their assignments, and when discussed, they revealed they were not aware of the ethical use of these tools.

The idea of the course redesign was driven by four key motivations. Firstly, students frequently submitted their portfolio assignments created solely by GenAI tools, although the lecturers did not allow this. The assignments were very conducive to completion with GenAI tools, while students did not know how to use GenAI responsibly.

Secondly, when students relied purely on GenAI in research design and did not put their design into practice, they failed to acquire the course learning goals. They could neither critically evaluate the output generated by such tools nor make informed and independent decisions. Thirdly, students are expected to use their learnings from this course in any other research activity in the study program and afterward. Finally, there have been initiatives at the university level to build an AI-literate community for the last few years, and contributions at different levels are expected.

Furthermore, lecturers at the university are encouraged to collaborate with educational teams and stakeholders to pilot initiatives that develop students’ AI literacy and integrate GenAI into courses. With this aim, the first author received an education innovation grant on a project called “Responsible AI in Academic Research” by the university and a team of lecturers, an assessment specialist and teacher trainer, an instructional designer and student assistants redesigned the course. The redesign included adjustments to the learning goals, the didactic approaches and the assessments.

A total of 37 students signed up for the course in the academic year 2024–2025. Most of the students were first-year students of the bachelor’s program. A few students took the course for their pre-master’s education, which is an academic preparation program for the master’s programs in the Department of Culture Studies.

Students’ ages ranged between 18 and 23, with the majority being female. The group was a mix of Dutch and international students. Students indicated at the beginning of the semester that they had no or limited background in academic research. Besides making use of LLMs in their daily lives and feeling at ease with them, they had no or limited background in the use of AI in research design. They expressed their interest in learning how to use GenAI in a responsible way.

Ethical approval to evaluate the intervention in the 2024–2025 academic year was granted by the Research Ethics and Data Management Committee of Tilburg School of Humanities and Digital Sciences (REDC 2024.47).

Mini-surveys, classroom observations and focus groups were used to explore how students experienced group work using GenAI tools. Using multiple data collection instruments enabled the researchers to triangulate their data. Mini-surveys were utilized twice during the semester, right after students completed their group assignments. They allowed the researchers to collect data in a quick and efficient way. Focus group discussions were implemented at the end of the semester to obtain in-depth insights from students. Classroom observations were made when students were engaged in group work and made use of GenAI tools. These observations enabled the researchers to observe and capture the process of what was happening in the classroom.

The mini-survey was adapted from the AI-Ideas, Connections and Extensions (ICE) Framework by Wood and Moss (2024) to explore students’ AI usage and modified from “Was It You or Role Conflict?” by Forsyth (2019). It consisted of 15 statements: nine addressed individual experiences with GenAI, while the remaining six focused on perceptions of their group’s use of GenAI. All items were measured on a five-point Likert scale (1 = mostly disagree, 5 = mostly agree). Upon completion of two group assignments, students filled out the mini-survey.

Upon completion of the course, three focus group discussions were held. The semi-structured interview guide included eight open-ended questions about how the groups collaborated using GenAI tools, what advantages and disadvantages they faced, how they experienced the responsible use of GenAI and how the use of GenAI influenced the group dynamics. Invitations were sent to randomly selected groups. A half-hour interview was held on Microsoft Teams. Due to circumstances, focus group 1 consisted of one member. Focus groups 2 and 3 consisted of all three group members.

Four observations were conducted in November and December 2024. The observations took place in the lectures, where students worked in groups and were allowed to use GenAI tools. Three observations were non-participant, with the observer positioned in the front corner of the room. The fourth was participant observation, during which the observer engaged with the participants by asking open questions. The observations were unstructured. The researchers made written records of any questions, answers and statements of students in interaction with each other during the group work as well as during the class discussion around GenAI.

Descriptive statistics were used to analyze the mini-survey data, given that the goal was to gain an understanding of how students perceive and respond to the use of GenAI in group work. Focus group discussions were recorded using Microsoft Teams and transcribed verbatim using the platform’s automatic transcription tool. Recordings were re-listened to ensure accurate and meaningful transcription.

The written records of the observation notes were reviewed by the researchers. An inductive thematic approach (Braun and Clarke, 2021; Kvale and Brinkmann, 2015) was employed to analyze the focus group discussions and the observation notes. The researchers read each transcription and observation note carefully to familiarize themselves with the data, coded inductively and selected quotations on the relevant and frequent themes.

Triangulation was ensured by collecting and analyzing data separately and then by comparing findings to ensure they covered the research questions sufficiently.

This section provides an overview of the results, starting with the redesigned course and then presenting how students experienced the new course design.

The course redesign involved adjustments to the learning goals, didactic approaches and the assessments, based on the university’s design principles of constructive alignment and blended learning.

The three pillars of Tilburg University (2017) were used in the redesign of the learning goals. As demonstrated in Figure 1, knowledge and understanding of responsible research were added to the existing goals about the three research paradigms, research methods and the empirical cycle. The learning goal on responsible research included conducting ethical research and understanding AI technologies for research purposes. The scope of the course content was reduced to ensure a more manageable study load and promote deeper learning (Biggs and Tang, 2011).

As Figure 2 demonstrates, in terms of skills, since students were required to apply the steps of the empirical cycle in group assignments, collaborative and communication skills were added to the existing learning goals.

In terms of character, as shown in Figure 3, developing a critical mindset and intellectual dependence was significant, so goals about acknowledging one’s own intuitions and prejudices when choosing and applying research methods and taking a critical stance towards the normative aspects of research methods and research paradigms were added.

To achieve the learning goals stated above, adjustments were made to the didactic approaches. Students were provided with a variety of learning activities based on three key forms of blendedness: (1) delivery mode, offering both on-campus and online learning experiences; (2) learning format, combining individual work with collaborative group activities; and (3) technological integration, using both AI and traditional tools (e.g. search engines, library databases).

Firstly, a lecture on responsible research was designed to familiarize students with the topic. This lecture aimed to have students name the key elements of responsible research, identify potential ethical issues and dilemmas and explain why GenAI tools must be analyzed carefully in research design. Before the lecture, students completed an e-module on GenAI, which was created by the TLC Science from the University of Amsterdam, and adapted for Tilburg University by the AI Steering Group of Tilburg University (AI Steering Group of Tilburg University, 2024). The e-module included how GenAI and LLMs work, opportunities, limitations, ethical and responsible use. During the lecture, students were engaged in group discussions on responsible research through questions directed by the lecturer.

Secondly, several group work activities were added in two forms. The first one was group discussions during the lectures. The second one was about forming groups to work towards completing different steps of the empirical cycle in and outside the class. Groups were formed based on similar research interests, and these groups remained the same throughout the semester. Group assignments consisted of detailed descriptions in which both individual contributions and joint acts were required.

Thirdly, several materials were developed. These materials included examples of GenAI output for different stages of research design, comparisons of prompts for the same task and discussion questions to help students think critically and reflect on the opportunities, limitations and ethical aspects of their assignments.

For the academic year of 2024–2025, the assessments consisted of group assignments (pass/fail), an individual reflection paper (50%) and an individual exam (50%). There were three big group assignments. Each assessed one of the research methods (survey, interview and narrative analysis) covered during the course. Each big assignment was also divided into sub-assignments with individual or group contributions. The group tasks were designed based on the taxonomy of group tasks by Forsyth (2019). Table 1 presents an example of a group assignment that requires individual and group contributions.

In this assignment (Table 1), students were asked to do a literature review on their research topic. Before the lecture, students were requested to find five research articles and summarize them, with or without GenAI. During the lecture, the groups reviewed each other’s summaries, with the guidance of the lecturer. After the lecture, each group submitted their literature review with summaries, justifying their selection. No grade was attached to the group work. The lecturers marked this assignment as complete or incomplete.

The individual exam and the reflection paper took place at the end of the semester. The individual exam included a question about responsible research, and the reflection paper required students to showcase their critical stance towards different aspects and stages of research methods. A minimum grade of 6 out of 10 was required for each assessment to successfully complete the course.

Table 2 presents the findings of the mini-surveys. The first mini-survey (N = 16) showed that students were aware of the limitations and potential biases of GenAI tools and that GenAI could not replace their critical thinking. Although they could critically evaluate the GenAI output before incorporating it into an assignment individually or as a group, they did not want to take the lead in initiating the use of it.

In the second mini-survey (N = 9), the mean scores were higher than the first mini-survey for the questions about sharing knowledge, suggesting strategies and critically evaluating the content individually and as a group. The mean scores of the statements about taking initiative and leadership in a group and about the use of GenAI tools for their group assignments were still low compared to the other statements.

To summarize, students gained knowledge of the responsible use of GenAI, and they gained confidence in the use of such tools critically during the course, yet they were hesitant to take the lead in group work.

Focus group discussions supported and extended upon the findings of the mini-surveys. Students who used GenAI recognized it as a helpful tool, and they could critically reflect on the use of it in responsible research, yet they were critical to use it in group work, as it could lead to trust issues in the group, and it could fail the whole group if not used responsibly.

Students who used GenAI had an overall positive experience. Outside this course, they started using GenAI for numerous purposes: summarizing articles and papers, explaining and providing examples of specific terms, decision-making, brainstorming and assisting with academic writing. In this course, groups seemed to turn to GenAI when they got stuck:

They described it as an outsider, yet one that could offer a different perspective to help them move forward. The lack of time to read multiple articles and manage various assignments and homework motivated students to use GenAI during group work. Groups that used GenAI remarked that highlighting the sections where GenAI was utilized made it easier to identify the sources of information. Students said that learning how to work with GenAI would be a useful skill to nourish and grow, “Because not all students are aware of how big of a difference one word in a prompt can make” (Focus Group 2, Participant 1).

Some students opted not to use GenAI, as they either lacked a reason to do so or had a negative attitude towards it, also stating they felt guilty, as it impacted their creative thinking and writing abilities. Concerns were raised regarding the responsible use of it in group work. Some participants suspected that their group members had used GenAI but chose not to admit it. Students also experienced glitches in GenAI output and made sure to check resources, reviewing each other’s work. The groups were uncertain about how to meaningfully process the output generated by GenAI. One group reflected on their group process:

Students desired to improve their AI literacy skills and suggested that groups should have an open conversation about GenAI before starting the assignment to establish boundaries.

Observations align with mini-surveys and focus group discussions. Although students used GenAI in group work, they were hesitant to acknowledge and embrace it, as they either lacked trust in each other and in GenAI, or they questioned the ethical aspects of it.

Groups used GenAI for grammar checks, brainstorming, simplifying terms, summarizing articles, finding resources and examples and structuring their thoughts. They had various approaches on how to use it. For example, for finding and validating resources, some groups turned to GenAI to locate relevant resources if they could not find them on Google Search and Web of Science. Some groups divided tasks and made sure one student verified GenAI output by finding the original resource, while in some groups, only one student interacted with GenAI and communicated the output with the whole group. Generating summaries of articles using GenAI was the most used activity. Students indicated a few times that using GenAI together taught them to recognize the writing style of GenAI, so they did not copy its output directly.

On the other hand, students found it challenging to use GenAI to divide the workload, as some tasks were easier for GenAI to assist with than others. Some students complained that group members did not always carefully examine the AI-generated content (a signal of social loafing) and were not always open about it. Having practiced GenAI enough to recognize it in the assignments resulted in frustration, as it would require more work and rewriting for the group members who felt the responsibility. Finally, as GenAI was banned in most of the courses, students hesitated to confess to what extent they used it.

This study investigated how a research skills course was redesigned to incorporate GenAI in group work and how students experienced its use. First, the learning goals of the course were revised in alignment with the educational principles of the university, adapting them for knowledge, skills and character. Second, changes in the didactic approach were made. These changes included adjustments in the delivery mode of the course, a combination of individual and collaborative group activities and technological integration. Finally, course assessment was customized by adding group work as formative assessment besides two individual summative assessments. Group work consisted of multiple assignments, and students used GenAI in different steps of the assignment.

Furthermore, the experiences of students were recorded via mini-surveys, focus group discussions and observations at different stages of the course. Mini-surveys indicated that students were aware of the limitations of GenAI. Although they used GenAI tools in group assignments, they did not prefer taking the leadership in the group. Student experiences indicated that they obtained knowledge, skills and characters outlined in the learning goals for the responsible use of GenAI tools. They demonstrated awareness of the possibilities and limitations of these tools and were able to critically evaluate them.

Focus groups showed that students had varying experiences and perceptions of the use of GenAI. This finding aligns with patterns observed in industry group work (Bezrukova et al., 2023). Students who used GenAI had positive experiences, and they could critically assess its output. They also indicated GenAI might negatively impact their creativity, evoke trust issues in the group and demand more responsibility towards each other.

The focus group results explain why students hesitate to take the lead in using AI tools in group work. For most students, it was the first time they used GenAI in a formal learning environment, so they may lack the confidence or certainty in their AI literacy to take on the responsibility and accountability (as required by leadership) for ensuring ethical and responsible use. Besides, group members may hold different levels of acceptance towards AI tools, and taking the lead could create additional coordination challenges.

Observations revealed frequent discussions between students and lecturers on GenAI, from permitted uses to ethical considerations, including human verification and critical evaluation. Although students were well informed about the permitted uses of AI, some still reported feeling “guilty” when using GenAI for certain tasks. This finding echoes the concern that, when disclosed through technology statements or acknowledgments, AI involvement may be perceived as deviating from expectations, creating a sense that such practices are inappropriate because they diminish human agency (Martin and Waldman, 2023). Furthermore, the frequent student questions highlight the importance of a shared understanding of transparency that goes beyond the formal AI disclosures (Schilke and Reimann, 2025).

Our findings show that students tended to use GenAI tools in group work depending on the task (e.g. making summaries, finding and understanding literature). However, the group dynamics were affected when transparency about the use of these tools was lacking, as limited trust among participants emerged. Students’ concerns about the reliability and accuracy of GenAI tools in group work mirror the findings of the study by Perifanou and Economides (2025), who indicated that students were apprehensive about the accuracy, reliability and currency of GenAI outputs. Students were aware that GenAI output needed cross-checking. Our observations were aligned with this, as group members cross-checked each other’s work when GenAI was used.

Integration of GenAI in group work is a multi-layered process that should consider many factors, beginning with the course design, implementation, assessment and student experiences. When given the knowledge and skills, students can take initiative in their learning activities in group work. Throughout the semester, students were provided with knowledge and understanding of responsible research using GenAI. Their progress was monitored closely by didactic activities and assessment. They were not merely aiming to use GenAI to get a sufficient grade (because no grade is attached to this group work), as they did not just accept it as a facilitating tool, but they were extremely careful with the use of it, making sure it did not replace their own work. As they were hesitant, they did not take leadership in the group. Although GenAI has been a prominent topic in educational discourse in recent years, its integration into practice remains limited. As a result, students, particularly those at the beginning of their studies, do not necessarily consider it a primary or immediate aspect of their learning experience. Whether higher education should position AI technologies as an inevitable part of the learning process requires careful consideration and design (Lee et al., 2024).

The results of this study can yield significant benefits to educators, educational experts, AI developers and policymakers. They contribute to an understanding of the use of GenAI in group work as well as the facilitation of course design using constructive alignment. Our systematic approach shows how to adapt course design for students to use GenAI in group work. Providing students with the necessary knowledge and skills supports the use of GenAI in a responsible way and prevents problems such as plagiarism, as students become aware of the limitations and consequences (Southworth et al., 2023).

On the other hand, integrating GenAI into group work also presents several challenges. First, group work using AI tools requires students not only to demonstrate disciplinary knowledge and thinking but also collaboration competencies (i.e. communication and coordination) and AI literacy. This combination can be described as collaborative AI literacy, defined as “a set of competencies that enables individuals to critically evaluate collaborative AI technologies; communicate and coordinate with them effectively and use them as a tool (online, at home and in the workplace)” (Sidra and Mason, 2025, p. 3). Focus group discussions indicated that students are interested in learning more about collaborative AI literacy. This suggests a valuable direction for future research: investigating how such literacy can be effectively developed and assessed within higher education contexts.

Second, building trust among group members is essential to ensure that everyone engages ethically and responsibly with GenAI. Future course designs should incorporate trust-building activities (Poort et al., 2022), such as clearly defining roles and responsibilities, scaffolding constructive group discussions and fostering awareness of cultural and experiential diversity, particularly regarding members’ prior experiences with GenAI and varying levels of AI literacy. Additionally, to prevent social loafing, where some members may become less attentive due to the absence of human oversight of GenAI-generated content, it is crucial to ensure shared accountability. Without this, the overall quality and credibility of the group’s work may be compromised.

Third, it is necessary to reconsider how group work is assessed and whether conventional assessment criteria and standards for final group products remain appropriate. In most group assessments, only the final product is graded, based on the assumption that high-quality outcomes result from effective group processes. However, our observations suggest that students often lack the skills to critically verify and evaluate GenAI outputs (Bearman et al., 2024) before integrating them into their assignments. Therefore, prompting students to apply quality criteria during the group process is essential to ensure the integrity and effectiveness of their collaboration. Process-oriented criteria, such as identifying quality academic sources and evaluating whether summaries are concise and accurate, are valuable for students to learn, as they contribute to producing stronger group outputs (Hsiao et al., 2023). An inclusive approach to GenAI tools, in which students of different skills, needs and interests can ensure that each student can make individual, independent and intellectual contributions to their group. When students are informed transparently about the possibilities and boundaries of GenAI use, they can also play an active role individually and collaboratively in preventive and innovative measures.

Finally, to benefit from GenAI technologies that take ethical considerations and sustainability into account in education, a wide range of stakeholders’ engagement is necessary.

We would like to thank all our students for their valuable contributions, Dr Mia Stokmans for their contributions on the course and Tilburg School of Humanities and Digital Sciences Tuned In projects for the financial support that made this research possible.