University of Turku

3.2 Misconduct in studies Misconduct refers to purposeful fraudulent action or disregard, and, as its result, the student gives a false impression of their own or someone else’s competence. It is also considered as misconduct when the student disregards the teacher’s instructions while working on the study attainment or uses forbidden means or tools. Using AI is also forbidden if the student aims to give a false impression of their own competence.

Collected: 2026-10-09 · 114–115
SHA-256: ee74f8ad76684362238320fe39db194eb263003e83bcbdbfcc3de44d69fdf5fd

Conditions & exceptions

Obvious lecture-hall cheating may be failed immediately with notification, without a full investigation; evaluation appeal remains.

4.1 Student's legal protection When investigating a student’s misconduct, practices of good governance must be followed. It is especially important that the matter is handled without undue delay and that the student has a chance to be heard before any disciplinary action is decided. The student must have a chance to bring a support person to the personal hearing, dean’s reprimand or other meeting organised by the University for handling the matter. The handling of the alleged misconduct is not public, but the decision documents are mostly public. Document publicity means that anyone can request to see the document or the information it contains. While the investigation is ongoing, the matter is brought only to the attention of the concerned parties and those persons whose work tasks it involves or persons who can provide additional clarification in the matter. The student must be informed about the progress of the investigation. 4.2 Procedure in handling misconduct This chapter describes the responsibilities and tasks of each actor in the investigation process. Teacher In this context, teacher refers to the teacher in charge of the course/thesis supervisor. The teacher reports the suspected violation to the administrative officer who investigates the matter (investigator) if the teacher cannot exclude misconduct or disregard for good scientific practice. In unclear cases, the teacher will first notify the student about their suspicion and give them an opportunity to give an oral or written explanation. If according to the teacher’s estimation the case involves a minor carelessness or ignorance on behalf of the student, the teacher gives instructions on correct practices and returns the work to the student for correction. In these cases, the matter is not forwarded to the investigator, but the teacher can consult them if they wish. The teacher cannot fail the study attainment on the basis of misconduct if the violation has not first been confirmed with the procedure described in this guideline (see the possible exceptional procedure for lecture hall exams below). When the teacher deems it appropriate to report the suspected misconduct to the investigator, the teacher gives their own written account on the matter. The evaluation of the study attainment shall be suspended until the matter is solved. If it is concluded in the investigation that the student has committed misconduct, the teacher must fail the study attainment. The student can appeal the failing grade similar to other evaluation of study attainments.

Collected: 2026-10-09 · 138–145
SHA-256: ee74f8ad76684362238320fe39db194eb263003e83bcbdbfcc3de44d69fdf5fd

Examination in a lecture hall (incl. electronic exams in lecture halls) In obvious cheating cases, the supervisor notifies the student about their observation immediately, asks the student to leave the exam, and a failing grade is given to the student for the exam. In these exceptional cases, the matter does not require instigating an investigation, as only a notification and a description of the event to the investigator is sufficient. To ensure the student’s legal protection, the student has the right also in these cases to make an appeal on the evaluation of the exam after the exam results have been published. Instead, if the matter concerns a suspected cheating, the exam supervisor notifies the student after the exam and notifies the responsible teacher of the course. In the notification, they describe the observation, the student’s possible explanation, and the taken action as well as their own written account on the matter.

Collected: 2026-10-09 · 149–150
SHA-256: ee74f8ad76684362238320fe39db194eb263003e83bcbdbfcc3de44d69fdf5fd

Conditions & exceptions

No automatic maximum sanction; unreasonable disadvantage exception to whole-course failure.

5. Disciplinary action for misconduct If it is concluded after the investigation that misconduct has taken place, the student receives a failing grade for the study attainment. The failing grade is the only disciplinary action when the student's misconduct is not very serious. Other possible disciplinary actions are dean's reprimand and the disciplinary actions described in Section 45 of the Universities Act, which include Rector’s written warning or a temporary suspension issued by the University Board. The disciplinary action also applies to the students who have assisted in the misconduct. If the suspected misconduct comes to light after the study attainment has been graded or the decision on the credit transfer/recognition of prior learning has been made, the suspected misconduct can lead to the rectification of the decision and disciplinary action. Confirmed misconduct is always reported to the student’s home university. This applies to both national and international mobility. Incoming students must be informed of the reporting procedure. The University’s guideline on good scientific practice and handling alleged misconduct is applied to doctoral dissertations. 5.1 Failing grade for study attainment If it is concluded after the investigation that misconduct has taken place, the student receives a failing grade for the entire course, unless it causes unreasonable disadvantage, for example, to patients or other external parties. 5.2 Dean’s reprimand In addition to a failing grade, the student can be summoned to a dean's reprimand as a disciplinary measure following the seriousness of the misconduct. Dean’s reprimand is issued every time the matter is handled on the University level. 5.3 Rector’s written warning If it is concluded that the student’s misconduct is serious, a Rector’s written warning is issued to the student. The dean proposes taking disciplinary action to the Rector. 5.4 Temporary suspension issued by University Board In serious cases or in the case of recurring misconduct in spite of a warning, the Board may suspend the student for a fixed period of time, which is at most one year.

Collected: 2026-10-09 · 173–185
SHA-256: ee74f8ad76684362238320fe39db194eb263003e83bcbdbfcc3de44d69fdf5fd

Conditions & exceptions

No court-appeal time invented; appeal does not imply automatic stay.

Rector Disciplinary action/procedure in accordance with TENK’s guideline is handled centrally at the University of Turku. The procedure is initiated when the dean proposes taking action to the University’s Rector. The Rector and the Director of Educational Affairs are responsible for the practical disciplinary action. The Rector and an expert in the Research Affairs are responsible for the disciplinary action in accordance with TENK’s guideline. The Rector decides whether the procedure according to TENK’s guideline is applied to the matter and the preliminary inquiry initiated. The Rector decides on issuing a warning in accordance with the Universities Act. If a warning is considered too lenient as a disciplinary action, the Rector proposes a fixed-term suspension to the University Board. The student can make an appeal to the Administrative Court on the decision. However, it has to be stated in the Board’s decision whether the decision is implemented while it has not yet gained legal validity.

Collected: 2026-10-09 · 165–168
SHA-256: ee74f8ad76684362238320fe39db194eb263003e83bcbdbfcc3de44d69fdf5fd

When the teacher deems it appropriate to report the suspected misconduct to the investigator, the teacher gives their own written account on the matter. The evaluation of the study attainment shall be suspended until the matter is solved. If it is concluded in the investigation that the student has committed misconduct, the teacher must fail the study attainment. The student can appeal the failing grade similar to other evaluation of study attainments.

Collected: 2026-10-09 · 145–145
SHA-256: ee74f8ad76684362238320fe39db194eb263003e83bcbdbfcc3de44d69fdf5fd

Conditions & exceptions

Commentary and policy distinguished; full linked teaching guidance not acquired.

Prohibited plagiarism becomes a permitted citation when it is properly referenced. The same applies to the use of artificial intelligence. The starting point is clear: the use of AI in learning is allowed and its use must be reported. Especially now when new technology is implemented, it is important to be as open and transparent as possible about how AI has been used in the thesis writing process. However, there are no established practices yet, and we are living in a time of expanding use and early experimentation. The University's common guidelines will be updated as we gain experience and insight. Study for learning, not for credits More important than formulating and following the guidelines is that everyone’s understanding and competence for the responsible use of AI is improved at the University. It is essential to remember that the primary objective of learning is to learn, not to pass a course and get credits. The aim of evaluation, on the other hand, is a fair assessment of what has been learned, not an approval or rejection of an attainment. I encourage students to use AI in their studies in responsible ways that they feel will help their own learning. Universities have freedom of teaching, and the teacher responsible for the course can encourage, instruct or restrict the use of AI in different ways. The aim of this kind of pedagogical guidance is to improve and enhance learning. Using AI on a course can also be prohibited altogether, when it is appropriate for achieving the learning objectives. The prohibition must be duly justified. Mutual trust is key to the interaction between teacher and student. Let our zero tolerance for purposeful misconduct create a basis for the ethicality of our actions and the equal treatment of students in creating a climate that encourages the responsible use of AI. I encourage students to actively seek their own individual ways of using AI to support their own learning. Different methods can suit different people and different situations. It is also recommended to share good practices, as this improves learning for the whole community. You should also share bad experiences. In the bigger picture, it is not only about the success of the individual, but also of the University and the nation as a whole, and even about the future of Europe. It is worth remembering that AI does not take people's jobs – instead, it seems that people who use AI insightfully take the jobs of people who oppose change. Responsibility for evaluating learning cannot be outsourced The AI revolution is shaking up not just students but also teachers. It has been reassuring to notice that, once again as a community, we are in the same boat facing a great change. AI offers a wide range of new tools for teaching, and also for the evaluation of learning outcomes. Some teachers are quick to embrace new technology and are excited to try out new methods, while others shy away from the very idea. I encourage teachers to experiment with AI in their work in a creative and open-minded way, but also to be tolerant of preference for old, tried and tested methods. It is hard to force creativity, and diversity can be a winning strategy for the community in this as well. Caution is particularly important when assessing academic performance: it has to be remembered that, as an employee of the University, the teacher exercises public authority over the students. The teacher(s) responsible for the course always have the authority and responsibility for the evaluation of the coursework and the related decision-making. This responsibility cannot be outsourced and AI cannot be the decision-maker. Keeping this in mind, I encourage teachers to adopt new ways of incorporating AI into their teaching and, with the reservations mentioned above, also to help them in the evaluation. The challenges that AI brings to evaluation are not always new. In group work-based learning, assessing individual competence on the basis of the group's output is a challenge familiar to teachers. In fact, the use of AI can often be compared to teamwork: if the thesis is produced in a collaboration between the student and AI, the challenges of assessing the contribution and skills of the parties are similar to those of traditional teamwork. In addition to the final result, it can be helpful to look at the process through version history, a learning diary or discussions with the students. In fact, the use of AI in the evaluation of theses is not entirely new either: plagiarism detection has been used for years. The idea is that the (AI) software shows the teacher the points in the thesis that should be checked when making the decision on approving the thesis. The program itself does not therefore make the decision on prohibited plagiarism. Specific AI software has also been developed to identify the use of generative AI. Their use is not forbidden either, and in some situations, they can be helpful in the same way as plagiarism detection: by focusing the teacher's attention on the relevant issues to be taken into account in the decision-making. However, there are problems with the use of AI, precisely from the point of view of responsibility in decision-making: AI must not be allowed to make a decision even on whether AI was used in the thesis. It may be that in many situations, common sense trumps artificial intelligence. The values of the University and the support of the community will carry us through this transition. To ensure that the use of AI is developed in the best possible way to support all work at our University – teaching and learning, research and administration – we have developed the University's own generative AI service chat.ai.utu.fi. The service was opened to staff and doctoral researchers in the summer, with the intention to expand its use to the whole community in a controlled way in the near future. The service makes it possible to use several commercial AI models as easily, safely and cost-efficiently as possible. I wish our entire University community inspiring experiments with AI in this darkening autumn, sincerely. Tapio Salakoski The author is the Vice Rector of the University, responsible for education and artificial intelligence.

Collected: 2026-10-09 · 91–105
SHA-256: 74338331e0e46f476fd0e2cd36c4ced138179b1e88ede3511ace3f0042c99a06