Using Generative AI in the teaching of academic writing

Project introduction and background information

In its Strategy 2030 document (Executive Board TU/e, 2018: 31), TU/e stresses the importance of digitization, to allow learning at any place, at any time and to support adaptive and personalized instruction and feedback. Intelligent systems could be used to fulfil this need, by providing personalized, automated and timely feedback.

The power, sophistication, and societal prominence of generative AI systems such as ChatGPT has only grown. As is evidenced by discussions in the academic, professional, and popular media, these systems pose an unprecedented challenge to established structures and institutions in many different domains, including higher education. The particular problem facing higher education is that generative AI undermines the principle of constructive alignment between learning objectives and assessment methods, impacting students and teachers alike.

For students, the advent of generative AI is poised to change the skills they require to succeed after leaving university and entering the workforce. Given their general-purpose nature, systems such as ChatGPT are likely to be used in many different domains, from software engineering to journalism and marketing. Graduating students entering these domains must possess relevant skills, which are likely to differ from the ones higher education is traditionally designed to promote. In particular, when performing writing tasks such as conducting literature review or compiling reports, students will no longer need to do so on their own, but will instead be expected to collaborate with AI technology to enhance the speed and quality of their writing. Among others, this will require mastery of skills such as prompt-engineering and machine summarizing, as well as a critical engagement with AI-generated content. Higher education generally, and TU/e specifically, should equip students with these skills, and will therefore need to identify and articulate “future-oriented” learning objectives to be achieved in writing-based university courses.

For teachers, the increased power and availability of generative AI challenges their ability to assess student learning. Because tools such as ChatGPT can be used to produce deliverables such as essays, reports, diagrams, and code, the provenance of these deliverables can no longer be traced to individual students as opposed to sophisticated machines. As a consequence, it is unclear that students are actually satisfying the stated learning objectives, and teachers will require “AI-proof” assessment methods that allow them to measure the extent to which students have mastered the ability to write clearly and effectively, either in collaboration with relevant AI systems, or on their own.

If TU/e is to stay at the forefront of societally relevant engineering education, it will have to promote the implementation of new education methods to promote effective writing. Crucially, this action will have to occur sooner rather than later: as generative AI systems become even more powerful, available, and easy to use, it is in the university’s interest to stay ahead of developments and be proactive rather than reactive.

Objective and expected outcomes

1. The main objectives and expected outcomes of the project are manifold.

WP1: Mapping the literature on future-oriented and GenAI compatible higher education

This work package will study the impact of Generative AI (GenAI) on aligning learning objectives, activities, and assessments in higher education. The main focus will be on identifying future-oriented learning goals and GenAI-compatible pedagogical and assessment methods based on literature searches. 

Expected outcomes:

• Report 1a: Report summarizing key insights about future-oriented learning objectives, and lists of learning activities and assessment methods useful to accommodate GenAI in education (Dec 2024). Closely related, research insights related to this WP will be presented at the 4TUCEE End of Year event (November 2024).
• Report 1b: An updated version of report 1a incorporating any relevant updates in light of AI technology advancements (June 2025).

W2: Designing a framework for learning assessment through AI interaction analysis.

This work package will study interactions between students and GenAI chatbots like ChatGPT and their relation with  learning outcomes. Collaborating with course teachers, education experts, and learning analytics specialists, we will develop a framework to analyze these interactions (e.g., logs) for multiple courses at TU/e that involve writing assignments. The focus will be on identifying patterns that indicate learning progress, GenAI literacy, critical thinking, and knowledge construction.

This analysis framework includes:

• Collecting student-GenAI interaction logs (user prompts and chatbot responses).
• Applying qualitative coding schemes for dialogue content to assess patterns of interaction, including critical thinking evidence, question types, and iteration patterns.
• Developing quantitative metrics for interaction analysis (e.g., rubric scores, input frequency). Conducting rubric-based assessments comparing traditional evaluations with GenAI-interaction assessments.
• Automating chat log analysis using natural language processing to identify learning patterns.
• Surveying student characteristics and perceptions through open-ended questions about their use of GenAI tools in learning.
• Collaborating with students to critically assess and optimize evaluation criteria for student-AI interactions (in alignment with TU/e BOOST objectives)

This working package will take place in AY 2024-2025 across various Bachelor’s and Master’s courses from different departments.  

Expected outcomes:

• Report 2a: A description of the methodology and framework for assessing learning outcomes through AI interaction log analysis as well as the outcomes from a set of pilot courses (April 2025). Closely related, research insights related to this WP will be presented at the 4TUCEE End of Year event (November 2024).
• Report 2b: Tutorial for teachers on practical implementations of the assessment framework.(April 2025)

WP3: Develop pedagogical activities powered by Generative AI

Based on the insights obtained from the literature review in WP1, as well as the insights gained from the development of the AI compatible assessment method from WP2, we aim to develop pedagogical activities to improve constructive alignment by teaching students how to effectively use AI to improve their performance on student-GenAI interaction assessments. The design of these GenAI tutors will be informed by insights gained in the previous work packages, and tailored to the learning objectives of the course and the specificities of the course assignments. These tools will be tested in pilot studies, in at least two courses with different types of assignments (e.g., argumentative essay vs. statistical programming and data analysis). For example:

• In a TU/e statistics course like “Advanced research methods and research ethics” where the objective for students is to learn how to think about their analytical approach to a given problem, the GenAI tutor can be designed to steer the student towards the assignment solution / desirable outcomes through questions posed to the student (scaffolding), as opposed to directly providing the solution. Simultaneously, a second GenAI tutor available to the same student could assist with another task required by the assignment, such as statistical programming, by providing explanations of the code or helping with code debugging. The behavior of the AI chatbot can be configured through prompt engineering techniques such as “Chain-of-Thought” combined with “Flipped interaction” (White et al., 2023).
• For writing-oriented courses such as “Data Science Ethics”, a GenAI tutor tool can be designed to assist with academic writing through scaffolding, such as guiding essay outlines, prompting critical thinking in the student, and offering instant feedback on their writing in a way that is aligned with the course learning objectives (embedded in the tutor’s underlying model or knowledge context). Likewise, the behavior of the AI chatbot is configured through prompt engineering techniques such as “Chain-of-Thought” combined with “Flipped interaction” (White et al., 2023).

By designing these GenAI tutor prototypes and incorporating them as tools in a course we will investigate:

• Whether using GenAI tutor tools lead to higher grades in student-GenAI interaction assessments, thereby providing insight into quality of constructive alignment in the course
• The impact of using these tools on self-perceptions of skill mastery (e.g., self-efficacy), thereby providing insights on alignment with students’ psychological
•  Students experiences using these tools (using open-ended questions in a post-assignment survey)

Expected outcomes:

• Report 3a: Tutorial on how to design a GenAI tutor and tailor it to a specific use case (March 2025)
• Report 3b: Report describing the results of the GenAI tutor studies (Nov 2025).

WP4: Workshops on GenAI tools for educational activities

AI literacy is a crucial step towards the responsible use of GenAI technology in educational practices (Kasneci et al., 2023; Redecker, 2017). In this work package, we will develop a workshop aimed at either or both students (ranging from Bachelor to PhD candidates) and staff where we teach how to properly design and implement a GenAI based assistants using the most relevant and/or accessible AI chatbot tool(s) at the time they take place. The workshop will focus on teaching the essential steps to configure the behavior of AI chatbot assistants through existing techniques (e.g. prompt engineering, fine-tuning options), and provide examples on how to tailor it to more specific use cases (e.g., drafting teaching materials, academic writing assistance, literature summarization, data analysis assistance). A second type of workshop will place a higher weight on practical tips to employ GenAI tools in academic writing activities. A third type of workshop, aimed for the ALT community (Academy for Learning and Teaching, TU/e) will focus on topics of AI literacy. Content (tentative) may encapsulate and chain the following topics: evidence-based utility of GenAI tools, building GenAI chatbots to augment teaching practice, ethical use of GenAI, plus any practical recommendations derived from insights from pilot studies. Dates and times of these workshops will be arranged with project members and project coordinators from either 4TUCEE (SEFI) and BOOST (ALT).

Expected outcomes:

• Ongoing workshops at TU/e event with focus on GenAI chatbot design for educational activities and where possible workshop at a scientific event (e.g., SEFI) (Dec 2024-Dec 2025).

References

• Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
• Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. In Y. Punie (Ed.), Technical report. Joint Research Centre (Seville site). https://data.europa.eu/doi/10.2760/159770
• White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT (arXiv:2302.11382). arXiv. https://doi.org/10.48550/arXiv.2302.11382

Results and learnings

WP1: Mapping the literature on future-oriented and GenAI compatible higher education

Report 1a

Introduction

The rapid advancement and increasing accessibility of generative AI (GenAI) technologies, and in particular ChatGPT (OpenAI, 2022) and its competitors (e.g., Anthropic’s Claude, Google’s Gemini, Meta’s Llama; First Page Sage, 2024), is transforming the landscape of higher education. As these tools become more sophisticated and widely adopted (Hu, 2023), they present both opportunities and challenges for educators and students alike (Cong-Lem et al., n.d.; Memarian & Doleck, 2023; Michel-Villarreal et al., 2023; Rahman & Watanobe, 2023; Ray, 2023; Stokel-Walker, 2022). The ability of GenAI to produce humanlike content, solve complex problems, and engage in natural language interactions is disrupting traditional models of teaching, learning, and assessment (e.g., Fleckenstein et al., 2024; Kelly, 2023; Rashidi et al., 2023). This reality calls for a critical re-examination and redefinition of educational objectives, focusing on the skills and competencies students will need in an AI-driven world. The disruptive impact that GenAI tools is exerting on Education calls for urgent transformations in educational practices and learning objectives. Higher education institutions are consequently pressured to innovate and adapt their curricula, pedagogical approaches, and assessment strategies to align with this new reality while maintaining the integrity and value of the educational experience.

In a world where technology is increasingly capable of mimicking human ability by producing output that is undistinguishable from human-generated output (Fleckenstein et al., 2024; Gao et al., 2023; Kumar & Mindzak, 2024; J. Y. Lee, 2023; V. R. Lee et al., 2024) there is an increasing pressure for educators (and education institutions) to explain to their prospective students what are the benefits of learning from a human teacher, ideally supported by evidence-based arguments. At the same time, students are increasingly pressured to convince their future employers that their skill set remains valuable against agentic technology like GenAI or any of its successors. To answer such questions, we need to take steps to identify which (human) skills GenAI technology is less capable of replacing, and which new skills should both educators and students need to start developing to adapt to the age of GenAI.

Another important challenge that educators are facing in the age of GenAI relates to the assessment of learning and skill acquisition. One of the main features of content generation technologies is their ability to generate content that is not only humanlike (Fleckenstein et al., 2024; Gao et al., 2023; Kumar & Mindzak, 2024), but sometimes judged as more real than real content (Rathi et al., 2024; Tucciarelli et al., 2022). This type of technology directly impacts the ability of teachers to assess student learning in cases where the assessed content can be easily generated by GenAI tools, as the mere technological possibility creates a constant uncertainty about content authorship. Although GenAI tools threaten the effectiveness of many teaching activities like multiple-choice quizzes, the uncertainty of authorship is especially evident when student output is in the form of written content like reports or computer code (Banić et al., 2023; Gao et al., 2023; Groothuijsen et al., 2024; Kumar & Mindzak, 2024). Given the evidence demonstrating the low ability of humans to reliably detect cues of AI origin in written content (Fleckenstein et al., 2024; Jakesch et al., 2023), the stronger the need becomes for rethinking how student learning can be assessed in a manner that is compatible with the possibility (or perhaps requirement) of content created collaboratively with GenAI tools. It is therefore also crucial to understand what assessment methods will remain capable of supporting and ensuring the integrity and value of learning, in an environment where the authorship of content is increasingly a mixture of machine and human inputs.

In this report, we compile initial insights from an ongoing systematic scoping review of the literature aiming to address the following two questions:

1. What future-oriented learning objectives are being identified as crucial in an AI-integrated educational landscape?
2. How are educators transforming their assessment practices and developing new evaluation frameworks in response to GenAI technologies?

Research Question 1: Future-Oriented Learning Objectives

In light of the pervasive influence of GenAI, higher education must prioritize the development of a range of cognitive, social, emotional, and technical competencies that will enable students to navigate and succeed in an AI-infused society. The recent literature emphasizes the importance of fostering skills such as critical thinking, problem-solving, creativity, and collaboration (Kasneci et al., 2023; Zhai, 2022). These higher-order thinking skills are essential for students to effectively leverage AI tools while maintaining the ability to think independently and generate original ideas. Additionally, digital literacy and AI literacy have emerged as crucial competencies, as students must learn to use AI tools ethically and responsibly, understanding their capabilities, limitations, and potential biases (Chiu, 2024; Zhai, 2022). Adaptability skills are also crucial in an environment where students are required to cope with and adapt to the continuously evolving landscape of AI-driven content generation technology (e.g., Bonfield et al., 2020; Chiu, 2024). By focusing on these future-oriented learning objectives and skills, higher education can empower students to thrive in a rapidly evolving technological landscape.

Below, we list the skills that were most frequently identified as relevant for the age of GenAI, followed by the skills that lost relevance as a result of the rise of GenAI.

Essential Skills:

• AI Literacy: Understanding GenAI capabilities, limitations, and ethical issues (Ng et al., 2023; Chiu, 2024)
• Higher-order thinking: Critical thinking, problem-solving, creativity, and analytical skills (Bower et al., 2024; Chauncey & McKenna, 2024)
• Learning with AI: Using AI tools for research and problem-solving (Mollick & Mollick, 2022; Chiu, 2024)
• Adaptability: Embracing new technologies and changing work environments (Chiu, 2024; Lim et al., 2023)

Skills Becoming Less Relevant:

• Knowledge Recall: Simple information retrieval and memorization (Bower et al., 2024; Kolade et al., 2024)
• Routine Tasks: Repetitive tasks like basic content generation or data entry (Kolade et al., 2024)

Brief commentary on the pace of GenAI development

As of the submission date of this report, developers continue to release AI models claiming record achievements in large language model (LLM) based "reasoning," which may be more appropriately described as a form of ersatz or surrogate reasoning. An example is the OpenAI o3 family of models, announced on December 20th, 2024 (Zeff & Wiggers, 2024). Whether or not that is the case, the capacity of this new model to produce output that can only be verified by a minority of experts is likely to amplify the impact of this technology on education. Some potential effects of the continuous increase in the capabilities of these models to mimic reasoning (whether or not they indeed reason, e.g., Amirizaniani et al., 2024; Kambhampati, 2024) might manifest as an increased possibility of students outsourcing of tasks requiring complex reasoning to GenAI, posing a constant threat to assessment practices, or at least those which are incompatible with GenAI (e.g., blended learning related assignments). Thus, one important question emerging in this discussion is also:

What are the human skills that GenAI is less capable or incapable of mimicking?

GenAI versus Human abilities

A quick search in the domain of grey literature provides us with some initial insights. In his article "Deep Learning Is Hitting a Wall," Gary Marcus (2022) argues that while LLMs are impressive in their ability to mimic human language, they are also fundamentally limited in their capacity for genuine understanding, reasoning, and truthfulness. Marcus (2022) emphasizes how LLMs struggle with common sense, logical inference, or handling novel situations, often producing nonsensical or even harmful outputs. These limitations result from their reliance on statistical patterns in immensely large datasets instead of on a true comprehension of the world, thereby rendering them unreliable in high-stakes scenarios (e.g. flying a plane, hiring decision, project planning This critique aligns with the concerns raised by Bender et al. (2021) in their seminal paper, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”, highlighting that these models, much like "stochastic parrots," can expertly mimic language without grasping its meaning. Bender and colleagues highlight not only the technical limitations but also the significant ethical and societal risks associated with the development and deployment of increasingly large and powerful LLMs.

In particular, Bender et al. (2021) point out that the sheer size and complexity of these models come with substantial environmental and financial costs, often concentrating power in the hands of a few resource-rich entities and potentially exacerbating existing inequalities. Moreover, they caution that the ability of LLMs to generate fluent and seemingly coherent text, while impressive, masks a profound lack of understanding. This can lead to the propagation of misinformation, the amplification of harmful biases, and the potential for malicious use, such as impersonation or the generation of deceptive content. Their work underscores the critical need for a more cautious approach that prioritizes ethical considerations, including the potential for social harm, alongside technical advancements. In essence, while Marcus (2022) focuses on the cognitive limitations of LLMs in terms of reasoning and understanding, Bender et al. (2021) broaden their critique to encompass the broader ethical and societal dimensions of deploying these technologies.

Together, these insights suggest that the path forward for (Gen)AI is not simply about building bigger and more powerful models but about developing systems that are more aligned with human values, capable of genuine understanding, and deployed responsibly within society (e.g. we can see glimpses of this latter behavior in the gradual release that most of these models undergo at launch in the past year). This perspective calls for a multidisciplinary approach that incorporates insights from computer science, linguistics, ethics, and the social sciences to minimize the disruption that AI development might cause otherwise, not only in education but to society as a whole.

The fundamental limitations of LLMs highlighted by Marcus (2022) and Bender et al. (2021) are further substantiated by recent empirical research. Amirizaniani et al. (2024) conducted a systematic investigation of LLMs' Theory of Mind (ToM) reasoning capabilities in open-ended scenarios, finding significant disparities between human and LLM reasoning processes. Their research demonstrates that even advanced models like GPT-4 struggle with nuanced social reasoning and complex open-ended questions, despite their impressive performance on structured tasks. This aligns with Kambhampati's (2024) view that LLMs essentially perform "approximate retrieval" rather than genuine reasoning, while emphasizing that their ability to generate coherent text should not be mistaken for true understanding or principled reasoning.

The results of this review, supported by empirical research conducted by Amirizaniani et al., (2024) and theoretical insights by Kambhampati (2024), offer compelling evidence for the transformative impact of GenAI on assessment practices in higher education. While large language models (LLMs) demonstrate impressive capabilities in generating human-like text, their fundamental limitations in reasoning, understanding, and alignment with human values necessitate a critical re-evaluation of learning objectives and assessment methodologies. The research of Amirizaniani et al. (2024) highlights the significant disparities between human and LLM reasoning processes, particularly in open-ended scenarios that require nuanced social cognition and complex reasoning. This aligns with Kambhampati's (2024) characterization of LLMs as performing "approximate retrieval" rather than genuine reasoning. These findings underscore the need for a multifaceted approach to assessment that recognizes both the potential and the limitations of generative AI technologies.

Kambhampati's (2024) analysis further suggests that the distinction between human and AI capabilities lies not just in what tasks can be performed, but in how they are performed. This has important implications for assessment design:

·        Process-oriented assessment: rather than focusing solely on final outputs, assessments should evaluate the reasoning process and methodology students employ.

·        Metacognitive skills: assessment should target students' ability to reflect on and explain their thinking processes, as Amirizaniani et al.'s (2024) research shows how LLMs struggle to fully incorporate human emotions and intentions into their reasoning processes.

·        Contextual understanding: Tasks should require students to demonstrate understanding across different contexts, as LLMs struggle with true transfer of knowledge despite their surface-level adaptability.

While LLMs excel at pattern matching and text generation based on statistical regularities in their training data (Bender et al., 2021), humans demonstrate empirically documented unique capabilities in complex social cognition, cultural learning, and theory of mind development  (Laland & Seed, 2021) LLMs notably lack capabilities in establishing the correctness of their outputs from first principles (Kambhampati, 2024) and true understanding of communicative intent (Bender et al., 2021). Educational design should focus on developing these distinctly human capabilities, particularly in areas of collaborative learning, cultural knowledge transmission, and social understanding.

The comparative analysis of LLM capabilities and human skills presented in Table 1 provides a framework for educators (and institutions) to guide their assessment practices and curriculum design in the age of GenAI. The educational implications emphasize the development of critical thinking, information literacy, authentic social interaction, innovative thinking, and responsible AI use, all of which are essential for students to navigate the challenges and opportunities posed by these emerging technologies. Furthermore, the current review highlights the importance of a multidisciplinary approach that integrates insights from computer science, linguistics, ethics, and the social sciences to ensure the responsible development and deployment of GenAI in education. As Bender et al. (2021) argue, the ethical and societal dimensions of these technologies must be prioritized alongside technical advancements to mitigate potential risks and promote equitable outcomes.

Future research should focus on developing innovative assessment strategies that align with the unique capabilities of human cognition, such as process-oriented evaluation (e.g. analysis of student-GenAI interactions through prompt analytics), metacognitive skill assessment, and contextual understanding (e.g., through analysis of how students infuse their knowledge in interactions with technology). Additionally, investigating effective methods for cultivating AI literacy among students and educators, examining the evolving landscape of teacher assessment literacy, and incorporating diverse stakeholder perspectives into AI-aware educational policies are crucial areas for further exploration.

Research Question 2: Transforming Assessment Practices

Traditional assessment methods, such as essays and exams, are increasingly vulnerable to the disruptive effects of GenAI, which can easily produce content that mimics human-generated responses (Kolade et al., 2024; Rathi et al., 2024; Xia et al., 2024). For instance, Xia and colleagues (2024) emphasize that GenAI challenges the validity of traditional assessments such as essays by enabling students to generate high-quality content effortlessly. The ability to outsource cognitive effort to GenAI tools has been shown to disrupt the learning experience (Bastani et al., 2024) and content co-created with AI is perceived by the authors as less meaningful to them and to others (Campbell et al., 2024). On a similar vein, Kolade and collaborators (2024) highlight the limitations of existing summative assessments in detecting AI-generated content and propose shifting the focus from assessing knowledge reproduction to assessments of how students apply their knowledge and competencies. To maintain the integrity and effectiveness of assessments in an AI-infused educational environment, educators are therefore urged to adopt innovative strategies that minimize the impact of (Gen)AI-generated content and that promote a more authentic learning (Cheng et al., 2024; Kim et al., 2022; Xia et al., 2024). This technological shift demands a fundamental rethinking of assessment strategies to both maintain academic integrity and better evaluate students' actual learning and capabilities (Kolade et al., 2024; Xia et al., 2024). Rather than viewing (Gen)AI as a purely disruptive technology, educators can alternatively perceive it as an opportunity to transform their educational philosophies and practices and take steps to implement more authentic, process-focused assessment methods that can better prepare students for the age of AI (Cheng et al., 2024; Mollick & Mollick, 2022; Xia et al., 2024).

Our current review of the literature highlights some themes and recommendations regarding how educators can adapt their assessment methods to deal with the reality of GenAI in the classroom. Educators are urged to adopt innovative strategies that:

• Move beyond detecting AI-generated content (Kumar & Mindzak, 2024)
• Focus on process rather than product (Cheng et al., 2024)
• Emphasize critical thinking and problem-solving (Kolade et al., 2024)
• Promote authentic assessment (Charles Sturt University, 2024)

Practical Strategies for Redesigning Assessments (Charles Sturt University, 2024):

1. Incorporate authentic assessments
2. Promote critical thinking
3. Individualize assessments
4. Use live interviews or presentations
5. Design novel application-based questions
6. Implement peer assessments
7. Use frequent low-stakes assessments
8. Emphasize creativity and problem-solving
9. Integrate real-life situations and practical experiences

Learning Activities with AI

The effective preparation of students for an AI-augmented professional landscape necessitates a methodological reconceptualization of learning activities in higher education (Mollick & Mollick, 2023, 2024; Xia et al., 2024). Our analysis of the literature indicates that project-based and problem-based pedagogical frameworks offer particularly promising approaches for developing critical competencies through their emphasis on authentic task engagement and interdisciplinary collaboration (Essel et al., 2024). These methodologies facilitate the systematic development of higher-order cognitive skills while simultaneously providing structured opportunities for GenAI tool integration.

A strategic incorporation of GenAI technologies into educational activities can enhance learning outcomes through multiple mechanisms. AI-enabled adaptive learning systems show significant potential for optimizing instructional personalization through continuous calibration to individual learning trajectories (Kasneci et al., 2023). GenAI tools like ChatGPT and similar chatbots can be thoughtfully integrated into learning activities to increase student engagement and skill development. For instance, in knowledge-building activities, GenAI can serve as a springboard for idea generation and critical discussion (Chen et al., 2023). Students can use GenAI to generate initial drafts, explanations, or inquiry questions, and then collaboratively evaluate, refine, and build upon these AI-generated contributions. In programming courses, GenAI can be used for tasks like error checking, debugging, and code explanation, allowing students to focus on higher-order problem-solving skills (Groothuijsen et al., 2024). However, it is crucial to design learning activities that require students to go beyond simply using GenAI outputs (R. Deng et al., 2024). Project-based assessments and tasks that demand originality and the application of knowledge in unique contexts can help distinguish between AI assistance and genuine learning gains.

The current review leads us to the view that the strategic integration of AI technologies in education, when aligned with established pedagogical frameworks, will create learning environments that effectively prepare students for algorithmic-human professional contexts. This carries significant implications for how institutions should approach technology-enhanced learning while preserving core academic objectives.

Concluding remarks

The emergence of GenAI represents a transformative moment in higher education (Chiu, 2024; Kasneci et al., 2023). Essential insights include:

• Prioritizing distinctly human cognitive capabilities (Bender et al., 2021)
• Viewing AI as a collaborative tool rather than a replacement (Mollick & Mollick, 2022)
• Focusing on higher-order thinking skills (Bower et al., 2024)
• Developing a multidisciplinary approach to technological integration (X. (Nancy) Deng & Joshi, 2024)

Next Steps

The project will continue through three parallel threads:

1. Finalizing the systematic scoping review
2. Designing and implementing pilot studies on GenAI's impact
3. Compiling and analyzing data from ongoing pilot studies in TU/e courses

Recommendations for educators:

• Provide faculty development on AI capabilities (Kasneci et al., 2023)
• Update assessment policies (Xia et al., 2024)
• Focus on higher-order thinking skills (Chiu, 2024)
• Conduct continuous research on AI in education (Lim et al., 2023)

References

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Recommendations

WP1

Recommendations based on the literature review from WP1.

Higher education institutions should focus on redesigning their curricula and assessment methods to emphasize skills that AI cannot easily replicate. This means shifting from content-focused instruction to developing higher-order thinking skills through project-based learning and collaborative activities. Educators should integrate AI tools like ChatGPT into their teaching while maintaining clear guidelines for ethical use, helping students understand both the capabilities and limitations of these technologies.

Assessment strategies should move away from traditional essays and exams toward performance-based evaluation methods that demonstrate authentic learning and application of knowledge. This includes implementing more real-time assessments like presentations, group projects, and case studies that require students to demonstrate critical thinking, problem-solving, and creativity while applying their knowledge in practical contexts.

WORK IN PROGRESS...

• WP2: Assessing student interactions with GenAI in teh context of academic writing 
• WP3: Pilot studies using GenAI based tutoring applications in the classroom
• WP4: Workshops on GenAI tutor building, AI literacy, Co-working with GenAI

Practical outcomes

WP1

The insights gained from the systematic scoping review of the literature aiming to gather information on the state-of-the-art perspectives and interventions regarding  future-oriented learning objectives and AI-compatible assessment methods, informed the design of pilot studies at TU/e assessing the impact of using Generative AI chatbots on a range of outcomes ranging from student learning to teaching activities.

WORK IN PROGRESS...

• WP2: Assessing student interactions with GenAI in teh context of academic writing 
• WP3: Pilot studies using GenAI based tutoring applications in the classroom
• WP4: Workshops on GenAI tutor building, AI literacy, Co-working with GenAI