How do students remain intellectually responsible when AI can participate in almost every stage of their work?
Redesign learning and assessment so senior students must verify AI-assisted work, explain what they delegate and remain responsible for their decisions.
Senior students are heading towards further study and work where they will be expected to use AI, explain their choices and remain responsible for the result. A polished piece of work does not show what they understood, delegated or checked.
This course helps teachers redesign learning and assessment so students can use AI where it is useful while making their own judgement, verification and responsibility visible.
Design learning and assessment that make students’ judgement visible, even when AI is part of the work.
Session 1
Participants examine what knowledge is, how it differs from information and whether a language model’s output can count as knowledge. They use AI-generated claims to consider how a claim is justified, tested and made persuasive.
They also test a text AI’s limits by attempting to produce a confident but incorrect answer, an invented source or an answer that follows a false assumption. The purpose is to examine reliability through evidence, not fluency.
Session 2
Participants use AI as a challenger to an argument. They ask it to identify gaps in logic, raise counter-arguments or produce material for scrutiny, then evaluate its observations and decide which to accept, reject or investigate further.
The work includes identifying assumptions, testing logical validity and forming a well-founded position of one’s own. The AI produces material; the student evaluates the reasoning.
Session 3
Participants use a thinking log to identify where a student used their own thinking, where they leaned on AI and where the tool began to take over work that should remain theirs. They connect this with transparent documentation of AI’s role in a piece of work.
Applied to an existing substantial task, the session asks what students need to document in order to account for their process, authorship and final decisions. It does not rely on AI-detection tools as proof of honesty.
Follow-up
A 60-minute online review of a first trial. Participants bring a thinking log, student documentation or a task design question, then identify where responsibility is clear and where the task still needs revision.
Starts 18 February 2027
3 sessions: 18 and 25 February, 4 March 2027
Online
Starts 23 June 2027
3 sessions: 23 and 30 June, 7 July 2027
Online
Starts 16 September 2027
3 sessions: 16, 23 and 30 September 2027
Online
Starts 11 November 2027
3 sessions: 11, 18 and 25 November 2027
Online
Registering interest is free. A course run is confirmed once at least six participants have registered their interest. Until then, neither a place nor a course run is guaranteed. Booking terms →
Run the course with your own staff, on your own dates.
Session 1
Participants examine what knowledge is, how it differs from information and whether a language model’s output can count as knowledge. They use AI-generated claims to consider how a claim is justified, tested and made persuasive. They also test a text AI’s limits by attempting to produce a confident but incorrect answer, an invented source or an answer that follows a false assumption. The purpose is to examine reliability through evidence, not fluency.
Session 2
Participants use AI as a challenger to an argument. They ask it to identify gaps in logic, raise counter-arguments or produce material for scrutiny, then evaluate its observations and decide which to accept, reject or investigate further. The work includes identifying assumptions, testing logical validity and forming a well-founded position of one’s own. The AI produces material; the student evaluates the reasoning.
Session 3
Participants use a thinking log to identify where a student used their own thinking, where they leaned on AI and where the tool began to take over work that should remain theirs. They connect this with transparent documentation of AI’s role in a piece of work. Applied to an existing substantial task, the session asks what students need to document in order to account for their process, authorship and final decisions. It does not rely on AI-detection tools as proof of honesty.
Follow-up
A 60-minute online review of a first trial. Participants bring a thinking log, student documentation or a task design question, then identify where responsibility is clear and where the task still needs revision.
Every course we run is built on what research says makes professional learning change practice. Here is what that looks like in this one.
Shared examples of how contemporary AI works, where it fails and what changes when information is generated instantly.
You choose one substantial task or assessment to rework so that students' judgement stays visible.
Task analysis and collaborative design work show you where AI changes intellectual work, with guided discussion of your revised task.
You test the revised task with students, then use a live online follow-up session to refine your expectations on attribution, disclosure and accountability.
Three live online sessions, one week apart, move from understanding and interrogation to practical curriculum design. Teachers work with shared examples, apply the ideas to their own learners and receive structured peer feedback. An optional group follow-up supports refinement after the course.
The course family is informed by the OECD and European Commission AI Literacy Framework, including the Engage, Create, Manage and Shape dimensions, alongside Nordic AI literacy work including Faktabaari EDU. The Nordic Learning Company translates these reference points into age-appropriate classroom learning and professional judgement about when AI supports learning and when it replaces intellectual work students need to do themselves.
EUR 350 per participant, excl. VAT
Course materials, facilitation, a certificate of attendance and an online follow-up session.
Nick Praulins is co-founder of The Nordic Learning Company, where he leads our work with schools and educators, shaping the learning philosophy, pedagogy and programme design behind its education practice.
View profile →Marius Ketels is co-founder of The Nordic Learning Company, where he leads much of our work with companies and organisations, designing and facilitating programmes around leadership, teams and human performance.
View profile →You can move to another date or send a colleague at no cost. If you cancel more than four weeks before the start, we keep 30% of the fee; inside four weeks, we keep the full fee. If we cancel, you choose a full refund or another date.
Pairs well with
Practical AI fluency and the professional judgement to decide where AI belongs in teaching and learning.
Help children become curious, questioning and increasingly thoughtful about the AI already around them.
Build an app. Learn to teach app building.
EUR 350 per participant, excl. VAT
Starts 18 February 2027
