General principles
Artificial intelligence tools help students make learning more efficient and accessible. Technological innovations enrich the learning process, and their use could be embraced just as we have adopted calculators, spell checkers, the internet, and search engines. From a learning perspective, AI offers many opportunities, but also challenges.
- Lecturers inform students how the use of AI-based software is permitted in assessment, teaching, and/or homework. This information can be found in the extended course syllabus.
- AI applications may be used, for example, as a source of inspiration, as a tool to evaluate and refine one’s own ideas, for translation, or to support learning in the early stages of work. They may also assist in editing student-written text during the finalisation stage. However, AI applications must not be used for large-scale writing of theses (e.g. creating an entire chapter), fabricating data for analysis, or generating substantive arguments.
- When an application is used purely as a tool (e.g. for text editing or translation, worksheet creation, test generation, or idea collection), no citation is needed.
- When output from an application is used in a substantive sense (e.g. a text excerpt from a chatbot, an image created by a generator), the tool must be cited as a method. In cases of more extensive use, the methodology or introductory section (or another suitable section) of the written work must describe the general way in which AI was used. The description must clearly explain the extent and manner of the application’s use.
- The student is fully responsible for the accuracy and quality of information, research material, and analysis results submitted for assessment, as well as for the correctness of referencing. Using AI applications as support tools at different stages of preparation is not prohibited, but it is essential to keep in mind that AI-generated text must not be presented as the student’s own ideas (or in general, as original content in academic writing).
- Artificial intelligence is not suitable as an independent source. Students must still reference actual human-created primary sources. AI-generated content may only be cited as a primary source if the purpose of the work is specifically to research or create AI use.
- The aim of student work, including theses and their defence, is to assess the learner’s acquired competences. The student is responsible for the content and quality of the thesis, regardless of the sources used, including AI.
- If completing an assignment requires the use of AI, it must be equally accessible to all. This must be considered especially if AI tools are paid services.
- In studies, only those AI technologies that comply with rules on personal data processing, data privacy, and cybersecurity may be used, and neither lecturers nor students may violate these rules. See also the Procedure for processing and protection of personal data.
Awareness and skills
Introductory course on the use of artificial intelligence
The course was created in 2025 at the request of the Study Department by the IT Didactics Centre and the TalTech AI Focus Centre. The aim of the course is to provide basic knowledge of the nature, functioning, and practical applications of artificial intelligence in different fields, with a focus on teaching. During the course, students are introduced to theory, explore use cases, and solve practical tasks to understand and apply the possibilities of artificial intelligence in everyday life and work. A total of 35 colleagues contributed to the preparation of the material, many of whom also share their personal experiences and examples.
Open coursePBL and Responsible AI course
In 2025, Tallinn University of Technology created a second e-learning course with the support of the Study Development Fund, aimed at practitioners of problem- or challenge-based learning who use or plan to start using artificial intelligence. The course was designed to provide an overview of how the authors have best applied it in teaching, as well as the perspectives of other colleagues and students.
Open courseAI Champions Workshop Program
This academic year, we are piloting the AI Champions Workshop program, which focuses on sharing the experiences of advanced AI users through practical workshops and seminars. The workshops are primarily intended for TalTech faculty and program managers, providing support for the conscious and responsible use of AI in teaching.
In general, the workshops last 1–2 hours, and at least one workshop on a different topic is planned each month, showcasing various applications of AI in teaching. This year, we will pilot a total of 10 workshops, conducted by AI Champions—faculty members who are ready to share their experiences with colleagues.
Registration for the workshops takes place through TalTech’s internal training calendar, where each workshop includes a detailed description, learning outcomes, and organizational information.
You can explore the AI Champions workshops currently open for registration under the events section.
Important materials and guidelines
Instructional materials for students at the start of the academic year
Programme directors: Explain to students how AI is permitted to be used in studies. Instructsions for program managers
Lecturers: AI instruction slides: Instructions for lecturers ENG
Artificial intelligence as an intelligent tutor – The student completes tasks step by step and receives individual guidance or feedback by asking the AI questions about the next steps, without the lecturer needing to intervene.
Dialogue-based tutoring systems – The student completes tasks step by step through a conversation in natural language. Advanced systems can automatically adapt to the level of engagement to motivate the student and keep them focused on the task.
Language learning applications – AI-based learning applications are used in both formal and informal education contexts. They support learning by providing access to language courses and dictionaries, and give real-time automated feedback on pronunciation, comprehension, and fluency.
Inquiry-based learning environment – The student is offered multiple representations to help them determine their own path toward achieving learning objectives.
Formative assessment of written work – The student regularly receives automated feedback on their written work/assignments.
AI-supported collaborative learning – Data about each student’s work style and previous results is used to group students either by similar ability levels or by complementary abilities and talents. AI systems provide information/recommendations on how the group is working together by monitoring the level of interaction between group members.
Support for writing written assignments – The student uses AI as a writing aid, asking it to provide a structure for addressing the chosen topic and to help with idea generation for a draft. The written work itself is produced by the student.
If you have any questions, feel free to contact the staff of your faculty’s didactics center and the educational technology center.
On the one hand, AI tools help to streamline and simplify various activities and processes, but on the other hand, assessing students’ independent work becomes a challenge, as detecting AI assistance is difficult. The challenge lies in designing teaching and assessment methods (including exams) in such a way that students learn to use future tools, while assessments still demonstrate their own knowledge and skills.
- Learning outcomes and assessment criteria can be reviewed and updated before the start of the semester in which the course will be taught.
- The syllabus must be available in ÕIS before the start of the registration period. The extended syllabus, including assessment criteria, is presented to students at the beginning of the teaching semester in the first lecture and is available in ÕIS.
- In the syllabus, it is possible to specify objectives, learning outcomes, a brief description of the content, and study materials. It is important to remember that if the course has previously been registered, the course title, volume, and form of assessment cannot be changed.
- When designing a course, it is worth considering whether and how to integrate new AI tools into your teaching.
- It is important that the teaching and assessment methods chosen in the course support the achievement of learning outcomes, meaning that the course is constructively aligned.
- Allowing or prohibiting the use of AI in a course must be specified in the extended syllabus. We recommend adding a subsection titled “Use of AI in the course” to the extended syllabus. Include information there on how you expect AI to be used in your course, how it may be used in assessment tasks, exams, and written work. If this subsection is not added to the extended syllabus, the university’s agreed “General principles for the use of AI in teaching” will apply.
- If, as a lecturer, you use AI assistance for feedback, assessment, and tutoring in the course, include this information in the extended syllabus.
- Assessment methods must, if necessary, be supplemented in such a way that they allow verification of the level of achievement of students’ learning outcomes and ensure that the submitted work has been produced by the students themselves.
- Students must be able to solve problems, and instead of checking only the solution, it is more useful to assess the problem-solving process and the student’s ability to evaluate it critically.
- In assessments, we recommend placing more emphasis on conceptual understanding of the subject and the ability to correctly formulate problems.
- When designing tasks, it is worth checking what answer ChatGPT (or another AI-based tool) gives – if the AI can solve the task correctly, the assessment format should be adjusted, for example by asking students to analyze how the AI contributed to the solution and what responses it provided.
- If solving a task requires the use of AI, this opportunity must be available to everyone. This must be considered when AI tools are paid.
- Students must reference the use of AI in their work as a method. AI is not suitable as a source, so students must still cite actual original sources to support their work. It is important to check a sample of the cited sources to ensure their relevance.
- The purpose of defending student work, including theses, is to evaluate the learner’s acquired competences. The student is responsible for the content and quality of the thesis, regardless of the sources used, including AI.
- If the lecturer wants AI not to be used in homework, this must be explicitly stated in the assignment instructions. Even though it is difficult to prove the use of AI, this gives students a clear signal that in this particular task, AI use is not considered supportive of their learning.
- It is worth discussing suitable assessment methods for the course with colleagues and experts at the didactics center. If a lecturer identifies a problem with academic integrity, it must be reported to the program director. The lecturer must be ready to substantiate their claims. No student will be found guilty of academic misconduct without evidence.
- The lecturer must treat all students equally and trust them – each student is responsible for their own work.
Here are some ideas to use if you want to assess students’ acquired skills rather than content generated by artificial intelligence. The list is not exhaustive, and a creative approach is welcome.
| CURRENT ASSESSMENT METHOD | ALTERNATIVE ASSESSMENT METHOD | NOTE |
| Written exam where the use of all aids is allowed | Oral exam
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For small- and medium-sized courses, it is possible to switch from a written exam (or reports) to an oral exam. |
| Instead of presenting questions in text form, ask students to explain diagrams/graphs/images in their own words (in writing).
The exam includes a task where an AI application must be used.
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Can also be used in large courses.
This type of question is harder to answer with AI, since AI applications are often not yet adequately capable of processing images. If AI applications are to be used in the exam, students must pose the right questions to the AI, which in turn requires an understanding of the topic. |
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| Report
Essay Presentation
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Report
Essay Presentation + a short oral discussion afterwards
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For courses where the exam takes the form of a report/essay/presentation prepared by an individual or a group, a short oral component may be added in which the student (or student group) explains the content and conclusions of the report in their own words within a few minutes (if possible by drawing a question or passage from the report at random).
For large courses, applying this method would require dividing the audience into smaller groups and involving multiple examiners. When presenting ideas and conclusions, the following should be observed: 1. During the presentation, all group members must speak (i.e., it is not enough for just one or two students to present on behalf of the whole group). The basis of the presentation can also be a poster, to which the same principles as for an oral presentation can be applied. |
| Mid-term exams consisting mainly of written work
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Add mid-term exams based on short oral presentations (or pitches) – and/or peer feedback. | Alongside written mid-term exams, it may be necessary to include components that ensure the student has produced the work themselves.
This can be done in the following forms: 1. add or replace some written assignments during the course with oral mid-term exams, or The oral part(s) could include, for example:
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| Group reports without an oral exam
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Group work must include a joint written confirmation from the group members stating that each of them has prepared their part of the report.
This can be supplemented with an oral presentation that demonstrates what each student has learned.
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The group is responsible for each member’s contribution. The assessment of an individual’s results is still based on the part that the person contributed to the report.
This can be checked in two ways: 1. by asking students to present their part of the group work orally to their group, so that other group members can take responsibility for presenting the whole group’s work; 2. groups provide feedback on other students’ projects (in similar projects) – preferably based on an oral presentation. Mutual feedback requires a deeper understanding of the subject and can improve students’ critical thinking skills as well as their understanding of how and what they themselves have learned. Peer feedback provides formative feedback to the students receiving it, which in turn supports learning. There are also other forms of peer feedback that can be used (and that support learning, even if they do not necessarily check whether AI has been used). These may be based on criteria developed either by the lecturer or by the students themselves. If peer feedback is included in the final grade, it is important to assess the student giving the feedback, not the one receiving it. |
| Written exam | Exam requiring pencil and paper (including multiple-choice exam) | Can also be used in large courses.
One option is to use a more traditional written exam requiring pencil and paper, conducted under supervision. This assumes that students have no access to the internet, computers, or other devices. Please keep in mind that students are not accustomed to doing their work using pencil and paper. |
| All exams or graded assignments
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Redesign the exam or graded assignment so that it also includes AI applications.
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As far as learning objectives and teaching (constructive alignment) allow, the use of AI applications could be part of the exam – the aim is to show the student how to use them while also being critical of the generated answers. The task or exam must be designed in such a way that it assesses the student’s knowledge and skills, not their ability to use AI (unless that is the specific goal of the exam). |
Purposefulness and responsibility
- AI tools are used only for purposes that support learning objectives and academic values.
- Lecturers are responsible for the appropriateness, quality, and impact of AI use in their course.
Transparency and explanation
- Lecturers explain to students why and how AI tools are used and what their limitations are.
- It is recommended to include an example or scenario of AI use in the syllabus to avoid misunderstandings.
Privacy and data protection
- Before adopting an AI tool, evaluate whether it meets personal data protection and cybersecurity requirements.
- Avoid entering sensitive or personal information into third-party AI systems without consent.
Equal access
- If an AI tool is required to complete a course, free or university-covered access must be ensured for all students.
- Avoid situations where economic status affects learning opportunities.
Fairness and bias reduction
- AI solutions should be selected and used in ways that do not reinforce stereotypes or discriminate against specific groups.
- If needed, test tools with different inputs to identify possible biases.
Academic integrity and competence assessment
- AI does not replace the student’s personal contribution; the purpose of assignments is to assess the learner’s knowledge and skills.
- Work that includes AI output must clearly describe to what extent and how AI was used.
Digital competence development
- AI use should help students learn to assess the quality of information, understand the logic of AI functioning, and recognize ethical risks.
- Whenever possible, link assignments with reflection on the impact and reliability of AI use.
Environmental impact awareness
- Acknowledge that using large language models consumes significant energy; avoid unnecessary and repetitive AI queries.
