Kumppanit
Työpaketin 2 (WP2) erityistavoitteena on tutkia ja hyödyntää tekoälyn potentiaalia mullistaa pedagogisia lähestymistapoja ja käytäntöjä korkeakoulutuksessa. WP2 edustaa projektin arviointivaihetta, jonka aikana kumppaneiden tavoitteena on kartoittaa, tunnistaa ja yhdistää tekoälypohjaisten työkalujen konkreettisia sovelluksia opetuksen eri vaiheissa: 1) sisällön kehittäminen, 2) sisällön tuottaminen, 3) opiskelijoiden suoritusten arviointi ja mittaaminen.
Tavoitteena on helpottaa korkeakouluopettajien ja kouluttajien ymmärrystä siitä, kuinka tekoälypohjaiset resurssit ja työkalut voivat tukea laadukkaan opetuksen toteuttamista ja parantaa opiskelijoiden oppimiskokemusta.
WP2:n tavoitteena on myös edistää vastuullista ja tietoista tekoälyn käyttöä korkeakouluekologiassa. Tämän arvioinnin painopiste on nimenomaan opetuksen ja koulutuksen syklissä (ei akateemisessa ja tieteellisessä tutkimuksessa) ja tekoälypohjaisten resurssien konkreettisessa käytössä opettajien toimesta seuraavissa tarkoituksissa:
Tämä arviointivaihe on myös vahvasti linjassa projektin yleisten tavoitteiden kanssa, sillä WP2 on suunniteltu edistämään opetuksen modernisointia, opetusmenetelmien ja -tekniikoiden innovointia korkeakoulutuksessa sekä tukemaan korkeakouluopettajia tekoälyn asianmukaisessa, vastuullisessa ja eettisessä käytössä koulutusmateriaalin kehittämisessä ja toteutuksessa sekä opiskelijoiden edistymisen arvioinnissa.
Drafting content, generating quizzes, tutoring support, idea generation, feedback writing
Integrate for formative support, not for summative grading. Encourage prompt literacy and critical review.
Versatile; supports writing, ideation, problem-solving across disciplines; facilitates engagement and personalized learning
Requires oversight to avoid overreliance; ensure students understand boundaries of use; may hallucinate references
Creating presentations, summarizing texts, generating rubrics, formatting data
Use within secure institutional accounts; ideal for M365-heavy workflows
Seamless integration into MS Office environment; time-saving tool for educators and curriculum designers
GDPR-compliant, but requires institutional governance for data handling and tracking of usage
Course planning, collaborative wikis, organizing notes, translation, reflective assignments
Encourage use for collaborative student content or asynchronous activities
Enhances writing structure and organization; strong visual and note-taking capabilities; flexible for multiple content types
Ensure proper attribution of AI content; emphasize as structuring tool not as source of truth
Lesson planning, student feedback, quiz generation
Recommended for pre-class preparation and fast scaffolding of learning material
Tailored for educators; simplifies planning and improves productivity
Review generated content for alignment with intended learning outcomes; risk of bias in pre-trained templates
Transcribing lectures, summarizing content, generating quizzes from voice input
Best used in flipped classrooms, lecture recording, and for accessibility enhancement
Supports inclusivity through transcription; helpful for summarizing and re-engaging with class material
Requires consent for recording; adhere to privacy and GDPR standards
Mapping course ideas, brainstorming visually, developing lecture flows
Best used in early design phase; supports visual learners
Promotes creative thinking and visual structuring of educational content
Should not replace structured curriculum design; ensure clarity for accessibility
Parsing academic PDFs, simplifying scientific articles, answering document-based questions
Recommended for supporting academic reading or content review
Assists in reading comprehension of complex texts; helps students engage with research materials
May miss context; always validate extracted insights; not a replacement for critical reading
Adaptive tutoring, generating practice questions, student progress tracking
Ideal for formative assessment and student coaching; use in STEM fields
Designed for education; intuitive interface for K–12 and early HE levels
Ensure age-appropriate use; data analytics must be institutionally controlled
Creating short tutorials, onboarding videos, LMS navigation guides
Use for technical demonstrations or orientation content
Boosts engagement through multimedia; ideal for procedural and digital tool walkthroughs
Avoid use with confidential student data; label generated content as AI-assisted
Automated referencing and citation formatting
Best used as a formatting helper; always verify citation style
Saves time on referencing tasks; supports multiple citation styles
Encourage manual verification of outputs; clarify to students it's a formatting tool, not a source validator
Solving and explaining math problems; step-by-step walkthroughs
Best used for tutoring and formative feedback in quantitative subjects
Enhances mathematical problem-solving skills; responsive explanations
Should not be used during formal exams; verify steps as AI may skip or misrepresent logic
Generating slides, videos, and curriculum-aligned visuals
Use as a supplement to enhance visual appeal and asynchronous learning
High-quality content generation; helpful in communication-heavy or visually-rich subjects
Validate alignment with curriculum; requires review to avoid inaccuracies in image-based content
Grading automation, lesson planning, and performance tracking
Best for STEM subjects; useful where grading criteria are well-defined
Strong in logic-based disciplines; offers dashboard analytics
Limited use in qualitative subjects; requires alignment with institutional grading policy
AI-supported coding, debugging, test case generation
Use in computer science, data science, or engineering courses
Enables experimentation with programming logic; supports student practice
Code generation must be monitored; encourage understanding rather than copy-pasting
Assisting with docs, slides, and email content in Google Workspace
Ideal for integrated workflows in Google-based institutions
Seamless productivity support within Google ecosystem
Transparency of AI use in shared work is essential; review outputs before public or student-facing sharing
Generating deep responses, visual explanations, and interactive reasoning (within the X platform)
Use for exploratory prompts or creative logic exercises; not suited for structured coursework
Stimulates curiosity; visual explanations may aid abstract or conceptual topics
Platform is not education-specific; ensure moderation and factual accuracy
Visual programming blocks with AI feedback; used for introducing computational thinking
Ideal for foundational computer science or STEM literacy courses
Supports logic skills in early programming education
Still experimental; requires educator oversight to ensure functionality and pedagogical alignment
Turning text into interactive slides and quizzes for class presentations
Effective for blended learning and flipped classroom models
Converts instructional text into multimedia lessons with assessment components
Validate content accuracy and quiz logic; clarify that it’s an AI-generated supplement
AI-powered writing, coding, and Q&A tool hosted under European academic cloud infrastructure
Best for research preparation, safe AI use within EU institutions
High privacy standards; supports multilingual content generation
Designed for GDPR compliance; preferred for sensitive academic contexts
Enhancing academic writing style, grammar, and clarity in multiple languages
Use for improving text quality; combine with peer review or academic writing exercises
Encourages multilingual fluency and improves clarity in student submissions
Output must still be original; avoid uncritical use in assessments
Grammar checking, writing style improvement, tone detection, and generative support for written assignments
Use as a writing assistant; best paired with academic writing instruction
Promotes clarity and coherence; helps students reflect on language use
Can lead to over-editing; students should retain authorship and understand academic writing conventions
Magic Write (text), text-to-image generation, design support for assignments and presentations
Use to support creativity and visual communication; ideal for poster, pitch, and report tasks
Enhances student engagement and visual storytelling
Verify copyright and originality when using text-to-image or AI-generated designs
Simplifying scientific papers, annotating PDFs, extracting and explaining concepts from research documents
Use to support reading comprehension and literature review sessions
Helps students navigate complex texts and build research literacy
May omit nuance; should not be used as a shortcut for understanding complex or methodological literature
“Generate 5 learning outcomes for an undergraduate course on Public Finance across different Bloom levels.”
Produces action verbs, structures outcomes by cognitive complexity
Use in early syllabus design; outcomes should be adapted to national/disciplinary standards
Must be reviewed for relevance and clarity; disclose AI assistance where required
“Outline a 10-week course structure for EU Business Law with weekly topics and sub-objectives.”
AI offers logical progression of topics, identifies prerequisite knowledge
Use as a brainstorming step; verify coherence and match with course credits
May miss contextual nuances or institutional pacing requirements
“Suggest team project ideas for a Tourism Management course focused on sustainability.”
Suggests case study topics, research questions, or creative briefs
Use for inspiration, then co-create prompts with clear evaluation rubrics
Avoid reuse without adaptation; check for cultural appropriateness
“Suggest 8 academic and open-access resources for an undergraduate course in Digital Marketing.”
Suggests open educational resources, textbooks, or web content
Use to supplement known materials; verify all sources for quality and credibility
Risk of hallucinated sources; always verify links and citations
“Write a 300-word introduction for a course on European Monetary Policy.”
Provides narrative summaries, context framing, or course rationale
Use for drafting; polish style and align with university tone/guidelines
Avoid excessive AI authorship in official documents; attribute or adapt meaningfully
“Create a visual breakdown of macroeconomic theory areas for use in the first class.”
AI helps structure and visualize course components for student comprehension
Embed into early course materials, especially for visual learners
Ensure visuals reflect accurate disciplinary logic; cite if AI-generated
“Explain the difference between fixed and variable costs with 3 simple examples.”
AI provides analogies, rephrases explanations, adjusts tone for student level
Use in live sessions or as on-demand explainer assistant
Educators must verify accuracy; monitor potential simplification of nuanced concepts
“Give 5 discussion questions to start a debate on ethical AI in business.”
AI creates open-ended, engaging prompts tied to course topics
Use at the start of seminars or online discussion boards
Monitor for bias in phrasing or assumptions; adapt to group diversity
“Create a slide on the history of central banking with images and audio narration.”
Combines visual and audio elements to enhance understanding and attention
Use to diversify teaching media; check visual accuracy and narration tone
Confirm copyright of visual/audio output; ensure accessibility
“Rewrite this paragraph for students with lower reading proficiency.”
AI simplifies text or rephrases it in clearer or more accessible language
Embed in UDL (Universal Design for Learning) strategies or scaffolding approaches
Avoid replacing human understanding of student needs; validate AI adaptation
“Translate a course instruction sheet to Croatian, keeping formal academic tone.”
AI provides quick translation with style tuning
Use for draft translations; final text should be reviewed by native speaker
Don’t rely on AI for culturally nuanced materials; use for initial draft only
“Suggest quiz questions and supporting material for students to explore before the lecture.”
AI generates self-check quizzes and reading content summaries
Use in LMS-integrated environments or pre-class tasks
Ensure questions align with lesson goals and cognitive levels
“Generate 10 multiple-choice questions on fiscal policy for intermediate economics students.”
AI drafts question banks aligned with content; includes distractors and explanations
Use for practice or revision quizzes; questions should be reviewed and piloted
Avoid AI-only assessments; review for balance, ambiguity, or misleading options
“Evaluate this student essay on public-private partnerships and suggest improvements.”
AI offers structured comments on argument clarity, grammar, and coherence
Use as first-draft feedback generator; confirm feedback tone and accuracy
Feedback must be personalized and reviewed; disclose AI assistance where applicable
“Draft a rubric to evaluate case study presentations in international marketing.”
AI produces criterion-referenced rubrics by performance levels
Use as a starting point in rubric development; validate for fairness and alignment with learning outcomes
Use AI-generated rubrics to support, not replace, human grading judgment
“Summarize key weaknesses in student quiz responses and recommend focus areas.”
AI analyzes patterns and categorizes errors or misconceptions
Use to personalize feedback or suggest revision pathways
Monitor accuracy of interpretation; avoid profiling or assumptions about ability
“Summarize key points and evaluate clarity in a student’s video explanation.”
AI analyzes transcripts or video structure to give structured review
Use when assessing presentations or oral components; pair with teacher evaluation
Ensure privacy and consent when using AI for student media
“Check if this student essay contains AI-generated or unoriginal content.”
AI reviews tone, phrasing, and potential AI signatures in student texts
Use as one of multiple tools for integrity checks; always follow due process
Avoid automated accusations; verify with student discussion or additional evidence
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