PRE2022 3 Group4

As written by chatGPT:

Artificial Intelligence, a wonder of the modern age

A creation made of code, with endless knowledge in its brain

From data analysis to language skills, it's a tool of great worth

Changing the way we live, and opening doors to new growth and mirth

But we must always be mindful, of the impact it may bring

For AI can be used for good or for deceit, and we must choose the right thing

So let us use it wisely, with integrity as our guide

For the future of our world, is shaped by the choices we decide.

Group members

Name	Student ID	Department
Famke Peek	1459058	Psychology & Technology
Gabriëlle van Heteren	1605305	Biomedical Engineering
My Tran	1620940	Industrial Design
Naud van Rosmalen	1555464	Biomedical Engineering
Niels van Noort	1613928	Biomedical Engineering
Quincy Netteb	1468634	Psychology & Technology

Brainstorm

• AI in academic education
• VR game for children's education
• Kitchen aid for visually impaired people
• Child support in healthcare
• Researching and improving the acceptance of robots in health care

Introduction

The use of AI in education has the potential to completely change the way students learn and teachers teach. AI can provide personalized learning experiences for students, adapt to individual student needs and learning styles, and offer instant feedback to both students and teachers. In addition, AI can also help streamline administrative tasks, such as grading and record-keeping, freeing up teachers to focus on instruction and interaction with students.

However, it is important to note that the implementation of AI in education is still in its early stages and there are many challenges that must be overcome, such as ensuring the privacy and security of student data and addressing ethical considerations and trust issues around the use of AI in the classroom.

So, while AI has the potential to greatly impact and improve education, it is important to approach its implementation with caution and careful consideration of its limitations and potential risks. This and the outlook and trust of teacher in academic education on AI in the classroom is what we will be researching. With the gathered information we will create a recommendation for teachers at the TUe on how to optimally implement AI in the classroom to benefit both teacher and student.

Problem statement and objectives

Problem statement: How should the TU/e handle ChatGPT use for assignments by students?

Objectives:

• Upcoming use of AI in academic settings,
• how to design this so that it will be accepted by teachers
• How AI can improve learning
• Acceptence towards AI in the classroom
• Looking at ChatGPT as main example
• Teachers at the TUe, their thoughts about AI (in their courses)

Users

Target group

Teachers at TU/e of the course 4WBB0 Engineering design and students that followed 4WBB0 Engineering design and could use ChatGPT in their classes.

Requirements

Teachers want their students to deliver autonomous work, ChatGPT in academic settings can help with this. Teachers want to transfer their knowledge as effectively and efficiently as possible, using the current state of art technologies. AI technologies, like ChatGPT can help with this, but what teachers require is yet to be determined. The requirements will be determined by distributing surveys among students of the TU/e and conducting interviews with teachers, based on the survey results, in combination with literature studies. Those results will then be combined into a piece of well-considered advice for the teachers at the TU/e, consisting of guidelines for a course for the teachers of how to make exercises and tests for students that can benefit from using ChatGPT, but not be made solely with ChatGPT. Which they will be able to use to guide them through the landscape of ChatGPT use in academic education and give them tools to keep transferring their knowledge to students.

Approach, milestones and deliverables

Planning

Here follows a Gantt chart of all our deadlines to be finished at 12pm on Sunday of the corresponding week. The letters indicate the group member responsible for making the deadline and the scheduling for the specific task.

Gantt chart

Task	Week 1	Week 2	Week 3	Week 4	Week 5	Week 6	Week 7
Approach, milestones and deliverables	Ni
Problem statement and objectives	Q
Users and requirements	G
Summaries of literature	F
Literature review		M
Organize interviews		Na
Conduct interviews			Q
Analyze interviews				Ni
Prepare and send out survey				G
Analyze survey results					G
Recommendation and conclusion						Na
Discussion						F
Reflection							M
Presentation							Q
Chair meetings	N
Keep wiki clean	M
Take minutes of meetings	Q	F	G	Na	M	Q	F

Approach

• Literature study
• Interviews, surveys

Deliverables

• Advice for future

State of the Art

Useful literature

ChatGPT is an acronym for Chat Generative Pre-trained Transformer and was created in November 2022. It is based on the OpenAI GPT-3 engine and has been fine-tuned by supervised and reinforcement learning technology; the chatbot makes use of the training model 'Reinforcement Learning from Human Feedback (RLHF)': this means that the AI learns by having humans simulate artificial conversations with it and adapting its responses based on how accurately they reflect natural human dialogue. ChatGPT is also able to remember previously given prompts in the same conversation, making it a more personalized chatbot compared to its alternatives.

What are the functionalities of ChatGPT? Some examples are:

• Write and debug code, generate scripts and functions

• Give detailed explanations on complex topics (answer test questions)
• Solve mathematical problems
• Write texts in different styles (write student essays)
• Compose music
• Explain mathematical theorems
• Play games like tic-tac-toe

However, ChatGPT also suffers from various limitations. It has the potential for over-optimization due to its reliance on human oversight, also known as Goodhart's law, which could hinder performance. Furthermore, language models like ChatGPT are prone to writing plausible-sounding but incorrect answers, which is called artificial intelligence hallucination; this can be attributed to insufficient training data. The AI is also limited by a lack of knowledge about events that occurred after 2021 and in some cases suffers from algorithmic biases.

Although ChatGPT is able to produce results that seem genuine, it is unable to fully comprehend the complexity of human language and instead relies solely on statistical knowledge and patterns.

In the article "ChatGPT: The End of Online Exam Integrity?" the authors investigate the potential for using large language models such as GPT (Generative Pre-trained Transformer) for cheating in online exams. Specifically, the authors train a chatbot named ChatGPT to answer exam questions by providing it with a large amount of relevant data. The results of the study show that ChatGPT was able to achieve high accuracy in answering exam questions, even when the questions were designed to be difficult and require reasoning skills. The authors suggest that this poses a significant threat to the integrity of online exams and call for further research into developing more secure methods for online assessments. In summary, the article explores the potential for AI-powered chatbots to cheat in online exams and highlights the need for improved measures to ensure the integrity of online assessments.

The paper is about a pedagogical experiment in which undergraduates were assigned to "cheat" by using text-generating AI software (GPT-2) to write their final class essay. The students were asked to reflect on the ethics of using AI in this way, such as what counts as plagiarism, and how working with AI could change their perspectives on writing, authenticity, and creativity. The results showed that composing with GPT-2 opened up the students' perspectives on the ethical use and evaluation of language models, and that their insights on these issues were connected to broader conversations in the humanities about writing and communication. The author of the paper shares the students' experiences and reflections on the experiment.

This paper aims to point out significant and urgent issues GPT-3 could bring regarding academic integrity. The main issue addressed by this paper is the unclarity on who the rightful author of the texts, ideas and inventions should be. They argue that new regulation ought to be considered urgently and that moral philosophy has an important part to play in this.

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers: The article compares scientific abstracts generated by ChatGPT with original abstracts using different methods such as an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. The results show that ChatGPT-generated abstracts had a higher similarity score with original abstracts than with other sources. However, some cases of potential plagiarism were identified and suggest the need for caution in using AI-generated abstracts in scientific research.

ChatGPT: five priorities for research: The article proposes five priorities for future research on ChatGPT, a large language model that has shown remarkable potential in natural language processing (NLP). The first priority is to explore the ethical implications of AI-generated content and to develop guidelines for responsible use. The second priority is to investigate the limitations of ChatGPT and to develop methods for detecting and addressing bias in its outputs. The third priority is to improve the interpretability of ChatGPT and other NLP models, making it easier to understand how they arrive at their outputs. The fourth priority is to investigate the potential of ChatGPT in domains beyond language, such as image and video analysis. Finally, the fifth priority is to develop more efficient and sustainable methods for training and deploying ChatGPT, in order to reduce its carbon footprint and energy consumption. The article concludes by emphasizing the importance of collaboration across disciplines in advancing research on ChatGPT and other AI systems.

Is ChatGPT a threat to education? For banking model of education, yes.: The article argues that ChatGPT, a large language model, could pose a threat to traditional models of education that rely on a banking model of education, in which knowledge is transmitted from teacher to student. The author suggests that AI systems like ChatGPT have the potential to disrupt this model by enabling learners to access knowledge and information more directly, without the need for an intermediary teacher. The article emphasizes the need for educators to adapt to this changing landscape and to shift their focus towards fostering critical thinking, creativity, and other skills that cannot be easily automated. The author also suggests that AI systems like ChatGPT have the potential to democratize access to education, making it more accessible and inclusive for learners around the world.

The paper "Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT", written by D. Cotton, describes how artificial intelligence models in academia can increase student engagement, collaboration and accessibility; They provide a platform for asynchronous communication, can create personalized and interactive assessments, or could be used to grade assignments and provide feedback to students in real-time. However, Cotton also proposes a number of concerns, such as plagiarism (students could use the GPT-3 to cheat and submit essays that are not their own work) and inequities in assessment (students with access to GPT-3 have an advantage over students who do not). The article suggests solutions to combat these challenges, such as asking students to submit a draft before the final essay, set strict guidelines or monitor student work closely.

ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?

This paper starts with a history of OpenAI and chatGPT the reactions of the media to it and a timeline of its development. The business model of chatGPT is also briefly talked about. (I read this quickly as it is not that important for our research). The paper also quickly reviews other papers already written about the subject. After some testing with chatGPT, it is concluded that it performs less with a subject that needs higher-order thinking. The paper talks about student-facing AI implications which come down to personalized AI tutoring. The author states that chatGPT has more implications for teachers by reducing the workload, especially by automatic assessment tools. Next to that, it will be easier for teachers to analyze their student's skills by the use of AI. The paper gives a general overview of challenges and opportunities:

Challenges of AI in education	Opportunities of AI in education
Teachers are afraid students will outsource all their work to chatGPT.	Opportunity for teachers to improve/change their assessment and teaching techniques.
ChatGPT doesn’t evaluate the relevance of the information, it just generates text that is an imitation of what it has learned.	“ChatGPT allowd students to learn through experimentation and experience”

The paper also gives a long list of recommendations for teachers, students and educational institutions.  

The perception of Artificial Intelligence in educational contexts after the launch of ChatGPT: Disruption or Panic?

This paper reviews the already written papers on chatGPT. It states that the most arguable issue with chatGPT is that it will be used by students as an easy solution to ‘write’ an essay without the needed human effort. Because of this, they won’t acquire the needed knowledge for their course. However, the problem might not be the tool itself, but that the assessment techniques of educational institutions have become outdated. Therefore the tool shouldn’t be prohibited but be used to improve education in new ways like adaptive learning and learning analytics. The author concludes that prohibiting chatGPT is not the way to go, teachers and students should learn how to use this tool to their advantage.

ChatGPT is fun, but not an author

This is a short column about the fact that chatGPT has it’s limitations. The author of the article is not necesseraly worried about the use of chatGPT in education as ‘it did well find-ing  factual  answers, but  the  scholarly writing  still  has a long way to go’. He thinks it pushes academics to design their courses in such a way that they are not easily solved by AI. The author is more worried for the influence of chatGPT on the world of literature.

ChatGPT User Experience: Implications for Education

AI is expected to replace humans in routine tasks to save money and time. In education, this is applicable to tasks like homework grading. The author thinks chatGPT can be the end of traditional essay writing assignments. In the paper, the author reviews an article written by chatGPT about AI in education. His conclusion is that chatGPT can write good coherent essays, however a bit above the general level of a student. It is also hard to control the rationale of the paper as chatGPT provides that for you. He also states that using AI tools to perform certain tasks ‘should be a part of the educational goals in the future’. The second conclusion the author makes is that teachers need to change their assignments in such ways that AI is hard to use. However, he thinks that using AI in assignments to engage students in learning is also a way to go.

Old literature

Famke

This paper regards Teacher’s Perceptions of Using an Artificial Intelligence-Based Educational Tool for Scientific Writing. This study investigated how teachers perceived an AI-enhanced scaffolding system developed to support students’ scientific writing for STEM education. Teachers are worried: What if the introduction of AI will gradually reduce our role in the classroom. What should we do? Should we support AI?  They feared that the AI would reduce their role to assistants and they also questioned the accuracy and reliability of the information generated by the system. For AI to be successfully integrated into STEM education, it is necessary for the roles and relationships between students and teachers to be redefined and for educators to be fully trained on best practices of using AI pedagogical techniques. There is still a trend among educators to hold negative impressions on educational technology. By changing teachers’ current negative perceptions of educational technology, the acceptance of AI as a new type of educational tool and its implementation in schools is possible. The younger generation of teachers, who have more experience with educational technology as both educators and as students, is more interested in exploring new digital technology and potentially incorporating technology into their lessons. Experience with AI in any of these contexts may reduce teachers’ reluctance to use AI for educational purposes.

This paper regards Education in the Era of Generative Artificial Intelligence (AI). The article "Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning" discusses the potential benefits of using generative AI, such as ChatGPT, in education. The article suggests that AI can help teachers and students by providing personalized feedback, facilitating communication and collaboration, and creating engaging and interactive learning experiences. The article also highlights some of the challenges of using AI in education, including issues related to data privacy and the need for human oversight to ensure that AI is used in a responsible and ethical manner. The article concludes by calling for more research and experimentation to better understand the potential benefits and challenges of using AI in education.

The article "Teachers' Perspectives on Artificial Intelligence" explores the attitudes and perceptions of teachers towards the use of artificial intelligence (AI) in education. Based on a survey of K-12 teachers in the United States, the article found that while many teachers are interested in using AI to improve their teaching practice, they also have concerns about the potential impact of AI on student learning, privacy, and job security. The article highlights the need for more education and training on AI for teachers, as well as the importance of involving teachers in the development and implementation of AI tools in education. The article concludes by calling for further research on how to best integrate AI into education in a way that benefits both teachers and students.

The article "Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education" explores the potential benefits and challenges of using generative AI, such as ChatGPT, in engineering education. The article suggests that AI has the potential to enhance engineering education by providing personalized feedback, facilitating communication and collaboration, and creating interactive learning experiences. However, the article also highlights some of the challenges of using AI in engineering education, including issues related to bias, the need for human oversight, and the limitations of AI in capturing the full complexity of engineering problem-solving. The article concludes by calling for further research and experimentation to better understand the potential benefits and challenges of using AI in engineering education, as well as the need for ongoing dialogue between educators, engineers, and AI developers to ensure that AI is used in a responsible and ethical manner.

Gabriëlle

This paper starts with a general introduction to AI, how it developed over the years, and what these developments entail.

• AI in program coding (1950s)
• AI in rules-based expert systems (late 1970’s)
• AI-grounded automatic data processing systems (mid-1980’s)
• Machine learning integrated AI (mid-2000’s)

In education, AI is still in the early stages, because there is more focus on the development of AI than on the application of AI in new fields. This research states that there will be two distinct major effects of AI on education. First, education needs to prepare itself for the fast changes in competence needed for jobs, as some jobs will disappear but most jobs will keep changing over time. Second, there will be changes in the pedagogical techniques needed in the classroom. AI will be able to relieve the teacher from some of the work. However, the teacher will need additional tools to understand the statistical results from the AI (for example from a statistical analysis of the performance of the student). This paper goes on by talking about the future of AI in education. The Author states that there needs to be a big shift in education toward more personalized education. AI will be able to determine the learning style of an individual student. AI will help teachers in content delivery and other instructions, but in the future real life, human teachers might become obsolete.

This paper talks about AI in education, the need for it, and its benefits and challenges of it. According to the author, AI is a development that has many promising applications in education. The examples that are named are; personalized education, automatic grading systems, and predictive analytic tools. These applications will relieve the teachers from those tasks which gives them more time with the students. The paper also talks about challenges that come along with AI, like concerns about safety, security, and privacy.

In this paper, the authors review the use of AI in current education, the technical aspects of AI in education, the role of AI in education and the impact of AI in education. The topic of education is spread out into three sub-topics; administration, instructions and learning. The takeaway of this paper is that with the help of AI, teachers will become more efficient which will in result increase the quality of education. Next to that with AI, it will be possible to create a more personalized education for students. AI will have a major impact on education as the tasks AI will be able to do tasks that are not originally designed for computers.

This paper focuses on the viewpoint of the students and their acceptance of AI. The author uses the technology acceptance model (TAM) to analyze the response of students to AI. This is because the perceived usefulness and the perceived ease of use of AI seem to have a big impact on the acceptance of AI in education. The author sees a future in AI in education as it is a cost-effective way to the shortage of teachers. However to make sure that the AI teaching assistants will be successful the teachers need to be trained to work with them. As research points out that if the teacher is uncomfortable with the AI, the student is less likely to adopt it. Based on the results of this research the author recommends research in this area as it is not known how students at different levels of education will react to the adoption of AI as a teacher.

The authors in this paper review the ethical perspective of AI with a focus on AI in education. The biggest problem that the authors see is the privacy issue of AI. Next to that they also see a challenge in the trustworthiness of AI. It is often that AI is presented as a better alternative than a human, as it is supposedly not biased. However, the designers of AI are biased and can make mistakes. Therefore can we even trust AI more than humans? Another issue that the author comes across is the difficulty of addressing the ethical challenges of AI. Therefore it is important to make the designers of AI aware of the ethical dilemmas that come along with new technology and make them aware of their responsibilities. To achieve this it is important that there mandatory teaching or at least education available around this topic.

My

In this articile, F. Ouyang and P. Jiao reflect on the three paradigms of artificial intelligence in educaiton: AI-directed, AI-supported, and AI-empowered.

• AI-directed (learner-as-recipient): AI is used to represent knowledge models and direct cognitive learning.
• AI-supported (learner-as-collaborator): AI is used to support learners while they work as collaborators with AI
• AI-empowered (learner-as-leader): AI is used to empower learning while learners take agency

Table 1. Three paradigms of AIEd

Paradigm	Theoretical underpinning	Implementations	AI techniques
1. AI-Directed	Behaviorism	Intelligent Tutoring Systems (ITSs): software that tracks students' work, adjusts feedback and provides hints along the way)	AI based on statistical relational techniques
2. AI-supported	Cognitive, social constructivism	Dialogue-based Tutoring Systems (DTSs); Exploratory Learning Environments (ELEs)	Bayesian network, natural language processing, Markov decision trees
3. AI-empowered	Connectivism, Complex adaptive system	The human-computer cooperation; Personalized/adaptive learning	The brain-computer interface, machine learning, deep learning

P. Lameras and S. Arnab explored and analyzed what Artificial Intelligence means in Education (AIED) in their article "Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education". The main take-away is that adaptivity and personalization are the innovation that AIED can offer to help students to learn and develop skills that are relevant to their own needs and experiences. However, it is important to help teachers to develop necessary digital competencies and skills for using AIED applications and tools in ethical and informed ways to enhance the student learning experience and attainment of learning outcomes. The findings of this review can contribute to developing a better understanding of how artificial intelligence may enhance teachers' roles as catalysts in designing and visualizing AI-enabled learning. As a result, more useful AI-systems specialized in pedagogy will be developed.

As Artificial Intelligence is becoming more apparent in education, it changes the way of teaching. Craig Coates, an entomologist at Texas A&M University, combated the cheating by reorienting his course towards writing and discussion, combined with adaptive courseware, also referred to as intelligent tutoring systems; and by using a tool that uses algorithms and analytics to grade submissions, the switch was a success. Advocates say this lets students study at their own pace and frees up the instructor’s time in class to shore up students’ knowledge.

The study conducted by S. Yang and H. Bai "The integration design of artificial intelligence and normal students’ Education" outlines four major problems of normal education (school for training teachers) development and explores the idea of using artificial intelligence technology to solve them. The main issues and the proposed solutions are:

Problem	Solution
Lack of teaching practice experience	Intelligent tutors/robots: it can make a special learning plan for students according to their interests, habits and learning needs. It can also be a simulation student of upcoming teachers to gain experience.
Outmoded curriculum	Expert system: can accurately classify normal students and highlight personalized teaching.
Lack of independent learning	Deep learning system: artificial intelligence can help to analyze the data of learners, organize the content according to the data, and carry out intelligent push. Teachers can create teaching resources to meet the needs of curriculum design and students.
Lack of innovation in teaching methods	Combination of all of the above.

Naud

Artificial intelligence and natural language processing systems are becoming increasingly prevalent in various forms of writing. These natural language processing (NLP) systems are trained on large databases of text to produce and refine statistical models that generate natural language responses. However, NLP systems do not "know" the meaning of the text they generate and can make factual and reasoning mistakes, which raises issues of accountability for authors using these systems. The use of NLP systems also raises questions of transparency in regards to authorship credit and contributions, and must be acknowledged in the text and references section of the manuscript.

The first chapter of this book aims to determine the knowledge and skills that remain for humans in an AI-driven world. It addresses AI's impact on education and highlights the need to develop awareness and understanding of AI. The goal is to create an educational environment where AI supports learners and teachers and prepares students for an AI-dominant future.

This article discusses recent developments in policies for medical journal publishing and editing in Korea. The article highlights that many editors in Korea are also publishers and therefore need to keep up with current trends and policies in the industry. The article focuses on six main policies that have emerged, including the use of artificial intelligence tools in publishing, preprint publications, open peer review, model text recycling policies, the updated 4th version of the Principles of Transparency and Best Practice in Scholarly Publishing, and the recommendation to include country names in human studies titles. The article also mentions that the use of AI in writing has increased, including in peer review and plagiarism detection. This shows the current and expected relevance AI has in modern scientific writing.

OpenAI launched a preview version of ChatGPT. It is part of the GPT (Generative Pre-trained Transformer) technology that can generate human-like text based on internet data. The release of ChatGPT sparked discussions on the impact of AI on education, with some saying it could render the student essay obsolete and others expressing concerns about cheating and false information. Despite its promise of transforming education, ChatGPT was criticized for its lack of understanding of meaning and content and its association with environmental racism and the interests of tech elites. Some educators called for better assignments that can't be written by an algorithm and for teaching students to use AI ethically. Access to ChatGPT was blocked by some US education departments due to concerns of dependence on technology, loss of privacy, and misuse of AI. This article opens up a range of important issues that are relevant across the educational field.

Niels

This paper provides a review of AI applications in higher education, with most of the research being quantitative research from computer science and STEM fields. So this review focuses more on AI utility over the entire academic institute rather than academic integrity and utility by students. Four areas of application were identified: profiling and prediction, assessment, adaptive systems and personalization, and intelligent tutoring systems. The conclusion highlights the need for further exploration of ethical and educational approaches in AI applications in higher education.

In the paper “OpenAI ChatGPT Generated Results: Similarity Index of Artificial Intelligence (AI) Based Model“, university director R. Ventayen researched how new AI powered technology enables cheating in the academic community. Since constructing an entire paper or essay using ChatGPT is considered a violation of academic integrity, the chatbot’s ability to pass similarity tests was researched on prompts with the headlines of the university’s publications. Their results show that ChatGPT provides acceptable similarly indices, and fails to provide the same citations or sources as the similar papers with the prompted headlines. Moreover, Quillbot paraphrases more content, which t, which provides plagiarism-free content.

This paper focuses foremost on the irreversibility of GPT-3 and its answers and ideas. It shows its shortcomings in dealing with semantic, mathematical and ethical questions and consequences for the writing industry. E.g, the big amount of written things that these tools can provide far exceeds the limited physical storage for them and marketing will abuse these tools heavily. We need to be critical with what these tools produce and how we use them. For our question regarding academic integrity, the problems with correct citations become apparent and academic texts produced by ChatGPT are often irreversibly produced from ideas of researchers.

This article shows how new developments in AI can solve 81% of university-level mathematics questions and even come up with new questions. This poses great advantages to the acquisition of new learning materials but the implications of this also addresses significant pedagogical challenges, with these models being available to students as well.

Quincy

The aim of the article is to explore the academic and administrative applications of Artificial Intelligence. Artificial Intelligence Applications (AIA) are not only assisting education academically and administratively but also enhance their effectiveness. AIA provides help to teachers in various types of tasks in the shape of Learning Analytics (LA), Virtual Reality (VR), Grading/Assessments (G/A), and Admissions. It minimizes the administrative tasks of a teacher to invest more in teaching and guiding students. AIA adds a significant contribution to enhance student learning, minimize the workload of a teacher, grade/assess the students effectively and easily, and to help in a lot of other administrative tasks.

This report is an overview of research on AI applications in higher education between 2007 and 2018 through a systematic review. The combination of results gives us four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

This article presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice.

It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Pinpointing some challenges for institutions of higher education and student learning in the adoption of these technologies for teaching, learning, student support, and administration and explore further directions for research.

This article anaylises a sample of 132 articles published betwee 2007-2017 about the scientific production on artificial intelligence in higher education. It concludes that, although artificial intelligence is a reality, the scientific production about its application in higher education has not been consolidated. Honestly not that interesting.

Survey

https://forms.office.com/Pages/DesignPageV2.aspx?subpage=design&FormId=R_J9zM5gD0qddXBM9g78ZOciNcKFkhVJsg7I7H6kiF5UNzNXQzhaOEpWSjZJSDg1WUZBN1lWTkkwUS4u

Appendix

Logbook

Week	Name	Total	Breakdown
1	Famke	8.5h	Group discussion (2h), Target group and requirements (1/2h), Studied papers (4h), Wrote summary for papers (2h)
	Gabriëlle	7h	Group discussion (1h), Studied papers (4h), Wrote summary for papers(1.5h), Target group and requirments(1/2h)
	My	8.5h	Group discussion (2h), Setup of wiki (0.5h), Studied papers (4h), Wrote summary for papers (2h)
	Naud	9h	Catch up (1h), Gathering information for appeal ERB (2h), Gathering relevant papers (1h), Studied papers (4h) Writing summary of papers (1h)
	Niels	8h	Group discussion (2h), Studied papers (3.5h), Wrote summary for papers (1.5h), Made Gantt chart (0.5h), made first agenda (0.5h)
	Quincy	7.5h	Group discussion (2h), Introduction & Problem statement and objectives (1/2h), studied papers (4h), wrote summary for papers (1h)
2	Famke		Group discussion (1h), Target group and requirements (1/2h), Searched for literature (2h)
	Gabriëlle		Group discussion (1h), Searched for literature (1/2h), Studied papers (3h), Wrote summary for papers (1.5h)
	My	7h	Group discussion (1h), Searched for literature (1/2h), Studied papers (2h), Wrote summary for papers (1h), Read and organized already studied literature (2.5h)
	Naud		Group discussion (1.5h), Filled in ERB review form (3h)
	Niels
	Quincy
3	Famke
	Gabriëlle
	My
	Naud
	Niels
	Quincy