A Responsible Way Forward for AI and Assessment

The answer to generative AI is not to ban AI completely.

The answer is also not to allow AI for everything.

Higher education needs a more balanced approach.

Students should learn how to work with AI because AI will be part of many professional environments.

At the same time, universities must preserve opportunities for students to demonstrate what they can personally understand, decide, create and perform.

A useful approach is:

Learn with AI. Perform with AI. Verify without AI.

1. Learn with AI

AI can be encouraged in formative learning activities.

Students can use AI to:

• Request explanations
• Generate practice questions
• Explore possible ideas
• Compare different approaches
• Receive feedback on drafts
• Prepare for presentations
• Identify gaps in understanding

However, students should not simply submit the AI-generated answer.

A stronger formative activity could require students to show:

• Their original understanding
• The AI feedback received
• Which suggestions were accepted
• Which suggestions were rejected
• Why particular changes were made
• What was learned from the process

This makes the student’s thinking visible.

2. Perform with AI

AI may also be allowed, or even required, in selected summative assessments when its use reflects professional practice.

However, the assessment must evaluate more than the final output.

Students should demonstrate their ability to:

• Select an appropriate AI tool
• Develop effective prompts
• Check AI-generated information
• Identify errors and hallucinations
• Recognise bias
• Protect private or confidential information
• Improve weak outputs
• Make their own decisions
• Accept responsibility for the final work

The assessment should not reward students simply for producing a polished AI-assisted report.

It should assess the quality of human judgement applied before, during and after the use of AI.

3. Verify without AI

At important points, students should still demonstrate essential capabilities independently.

This can be done through:

• A supervised test
• An oral examination
• A viva
• A live presentation
• An in-class problem-solving activity
• A practical demonstration
• A code walkthrough
• A laboratory activity
• A spontaneous case response
• A controlled written explanation

This does not mean every course needs a three-hour final examination.

It means every programme needs enough trusted assessment points to confirm that students personally possess the required knowledge and skills.

A practical decision should therefore be made for every CLO.

AI prohibited

Use this when the CLO requires independent and unaided performance.

Examples include fundamental calculations, essential safety knowledge, basic language competency or foundational problem-solving.

AI restricted

Students may use AI for limited purposes, such as language correction, brainstorming or feedback, but not for completing the core task.

AI permitted

Students may use AI, but must declare:

• The tool used
• The purpose of using it
• Important prompts
• Relevant outputs
• Changes made
• How the information was verified

AI required

The assessment intentionally measures the student’s ability to use AI critically and professionally.

For example:

• Critique an AI-generated business proposal
• Identify false references generated by AI
• Improve an AI-generated lesson plan
• Test and correct AI-generated code
• Compare human and AI recommendations
• Evaluate bias in an AI-generated decision

This is more useful than a general statement such as “AI may be used responsibly”.

Students need clear instructions for each assessment.

Institutions also need to look beyond individual courses.

Instead of trying to make every assignment completely AI-proof, programmes should identify important stages where student capability must be independently verified.

For example:

1. A foundational knowledge checkpoint
2. An intermediate application task
3. An AI-assisted professional assessment
4. A capstone project
5. An oral or practical verification

This combination can provide stronger evidence than relying entirely on either examinations or take-home assignments.

The future of assessment will not be a choice between traditional examinations and alternative assessments.

It will likely involve a purposeful combination of:

• Controlled assessments
• Authentic projects
• AI-integrated tasks
• Oral verification
• Practical demonstrations
• Portfolios
• Programme-level competency checkpoints

The objective is not to prevent students from using powerful tools.

The objective is to ensure that students develop the knowledge and judgement needed to use those tools responsibly.

Graduates must be able to work with AI, but they must not become unable to work without AI.

Ultimately, a university qualification must certify the capability of the graduate, not merely the graduate’s access to an AI tool.

Related articles:

>>> AI Is Changing How Students Learn. That Is Not Necessarily a Bad Thing.
>>> The Danger of Going All Out - Alternative Final Assessment in Higher Education

#HigherEducation #GenerativeAI #AssessmentDesign #AssessmentLiteracy #ResponsibleAI

When Good Work No Longer Proves That Learning Has Happened

For many years, lecturers could reasonably assume that a submitted assignment reflected the student’s effort, understanding and ability.

That assumption is no longer safe.

Generative AI can now help students:

• Interpret an assessment question
• Develop an outline
• Generate arguments
• Write a report
• Create references
• Produce presentation slides
• Develop computer code
• Write a reflection
• Improve the final language

The submitted work may look excellent.

But can the student explain it?

Can the student defend the decisions?

Can the student apply the same knowledge to a different problem?

Can the student recognise when the AI-generated answer is wrong?

This is the real challenge.

Universities may receive better-looking assignments while becoming less certain that students have achieved the Course Learning Outcomes.

Assessment is not only about producing work. Assessment is the checkpoint used to determine whether learning has taken place.

If AI performs the intellectual work expected by the CLO, then the assessment may no longer provide valid evidence of the student’s achievement.

For example, consider a CLO that requires students to:

Analyse a complex organisational problem and recommend an appropriate solution.

If AI performs the analysis, develops the recommendation and writes the justification, what exactly has the student demonstrated?

Editing the AI output is not necessarily the same as achieving the CLO.

This becomes even more serious when conventional final examinations are replaced with unsupervised take-home assessments.

A lecturer may:

1. Take the existing examination questions
2. Upload them into the LMS
3. Give students 24 or 48 hours to respond
4. Rename the activity “Final Assessment”

That is not assessment transformation.

It is simply an unsecured take-home examination.

The assessment loses the controls of a supervised examination, but it does not gain the strengths of a properly designed authentic assessment.

The result may be the worst of both worlds:

• It is not a secure examination.
• It is not an authentic assessment.
• It may not verify individual competency.
• It is highly exposed to AI-generated answers.
• It may encourage collaboration beyond what is permitted.
• It may produce unreliable CLO attainment results.

There is another uncomfortable truth:

Authentic assessment is not automatically AI-resistant.

AI can produce case analyses, lesson plans, marketing strategies, business proposals, research summaries, reflective writing, computer code and project documentation.

Adding a real-world scenario does not automatically make an assessment secure.

Even personalised questions can be uploaded into an AI tool.

        AI detection software will not solve the problem either.

Detection may raise concerns, but it cannot reliably tell us:

• Who completed the work
• How much AI was used
• Whether the use was permitted
• Whether the student understood the submission
• Whether the student personally achieved the CLO

The solution cannot be limited to catching students.

The better question is:

What evidence would convince us that this student has genuinely achieved the CLO, whether AI was used or not?

This does not mean universities should return entirely to conventional examinations.

It means assessment can no longer depend only on the final product.

A polished report is no longer enough.

A complete portfolio is no longer enough.

A professional-looking presentation is no longer enough.

Lecturers may also need to see the student’s:

• Development process
• Decisions and justification
• Response to feedback
• Ability to correct mistakes
• Application to a new situation
• Live demonstration
• Oral explanation
• Individual defence

Without these forms of evidence, universities may gradually produce graduates with impressive assignments but weak underlying capabilities.

The qualification may appear strong on paper while becoming less reliable as evidence of what the graduate can actually do.

In the next article, I will explore how institutions can respond responsibly without banning AI or allowing AI to replace learning.

Related articles:

>>> AI Is Changing How Students Learn. That Is Not Necessarily a Bad Thing.
>>> The Danger of Going All Out - Alternative Final Assessment in Higher Education

#HigherEducation #GenerativeAI #AssessmentIntegrity #AcademicQuality #CourseLearningOutcomes

AI Is Changing How Students Learn. That Is Not Necessarily a Bad Thing.

Generative AI has quickly become part of the higher education learning environment.

Students are using AI to:

• Explain difficult concepts
• Summarise reading materials
• Generate practice questions
• Brainstorm ideas
• Improve their writing
• Review early drafts
• Practise presentations
• Explore different solutions

Used properly, AI can become a powerful learning companion.

A student who is struggling with a difficult concept no longer needs to wait until the next class. AI can provide an explanation immediately, simplify the language, give examples and even create additional exercises.

For students who are less confident in writing, AI can help organise ideas and improve clarity. For students learning in a second language, it can reduce language barriers and allow greater attention to the subject itself.

This creates an exciting opportunity for higher education.

Instead of spending too much time reproducing information, students can spend more time:

• Asking better questions
• Comparing different perspectives
• Solving meaningful problems
• Evaluating information
• Creating new ideas
• Applying knowledge in real situations

AI can also make formative assessment more useful.

Formative assessment is designed to help students learn before the final grade is awarded. Examples include drafts, practice activities, proposals, discussions, quizzes and early project work.

In these activities, AI can act as a tutor, coach or feedback partner.

But there is an important condition:

AI should support the learning process, not replace the learning process.

If students use AI to explain a concept and then attempt the task themselves, AI can support learning.

If students ask AI to complete the task and submit the answer without understanding it, learning may not happen at all.

The difference is not whether AI was used.

The difference is how AI was used and what the student learned from using it.

The future of higher education cannot be built around banning every AI tool. Students will enter workplaces where AI is increasingly part of professional practice.

Graduates will need to know how to:

• Use AI productively
• Check whether its answers are accurate
• Recognise bias and hallucinations
• Protect confidential information
• Question inappropriate recommendations
• Make their own professional decisions
• Remain accountable for the final outcome

These are becoming important graduate capabilities.

The opportunity before us is not simply to help students produce better assignments. It is to help students become better thinkers and more responsible users of technology.

However, this opportunity comes with a serious warning.

When AI can produce an impressive assignment within minutes, can we still assume that the submitted work represents the student’s actual learning?

That is the concern I will explore in the next article.

#HigherEducation #GenerativeAI #StudentLearning #FormativeAssessment #EducationalInnovation

A Better Way to Introduce Alternative Final Assessment

If an institution wants to expand the use of alternative final assessment, the answer should not be:

“Remove all final examinations.”

The better instruction is:

Select the most appropriate final assessment method for each course and justify the decision.

The decision must begin with the Course Learning Outcomes (CLOs), not with a university-wide preference for or against examinations.

For every course, the faculty should ask:

1. What must the student know or be able to do?
2. What evidence would demonstrate that the student has achieved it?
3. Does the assessment need to measure individual performance?
4. Does the student need to perform under controlled conditions?
5. Can the work be completed using outside assistance or generative AI?
6. How will the institution confirm that the submitted work belongs to the student?
7. Can the assessment be marked consistently and fairly?

Based on these questions, a course may use one of three approaches:

1. Final Examination

Suitable when individual knowledge, procedural fluency, independent problem-solving or performance under controlled conditions must be assessed.

2. Alternative Final Assessment

Suitable when students need to create, design, investigate, perform, demonstrate, reflect or solve an authentic problem.

3. Hybrid Assessment

Suitable when both knowledge and application must be assessed.

For example, a course may use:

• A controlled test to assess core knowledge
• A project to assess application
• A presentation or viva to verify ownership and understanding

A hybrid model may be more academically defensible than completely removing examinations.

Before implementing the change widely, the institution should also:

• Establish clear definitions of final examination and alternative final assessment.
• Train academic staff in constructive alignment and authentic assessment design.
• Review each course individually rather than applying one rule to all courses.
• Develop clear rubrics and moderation procedures.
• Introduce checkpoints, presentations or viva sessions where appropriate.
• Address plagiarism, contract cheating and generative AI use.
• Pilot the approach in suitable courses before expanding it.
• Review grade patterns, student workload and learning outcome attainment.

Most importantly, lecturers should not be asked only to change the assessment label in the LMS.

Lecturers need time, guidance, examples and proper academic support to redesign the assessment itself.

The objective should not be to become an institution without final examinations. That is not, by itself, a sign of innovation.

The objective should be to become an institution that uses the right assessment for the right learning outcome.

Do not remove examinations because they appear old-fashioned. Remove an examination only when a better and more defensible assessment method is available.

Assessment transformation should be driven by learning, not by policy targets, administrative convenience or excitement over a new approach.

Go to previous articles:
> The Excitement of Alternative Final Assessment - Higher Education Perspectives
> The Danger of Going All Out - Alternative Final Assessment in Higher Education

#HigherEducation #AssessmentLiteracy #AuthenticAssessment #AcademicGovernance #EducationalInnovation

The Danger of Going All Out - Alternative Final Assessment in Higher Education

Alternative final assessment sounds progressive. But making it compulsory for every course across every programme can create serious academic problems.

The blunt truth is this:

Not every course should replace its final examination with an alternative assessment.

Some learning outcomes are best assessed through projects, portfolios, presentations or demonstrations. Other learning outcomes may still require students to demonstrate individual mastery in a controlled and time-limited setting.

This may be especially important in courses involving:

• Mathematics and quantitative problem-solving
• Accounting and financial calculations
• Statistics
• Law and legal reasoning
• Scientific principles
• Technical knowledge
• Professional or regulatory requirements

A project can assess application. A presentation can assess communication. A portfolio can show development over time.

But these methods may not always prove that a student can independently recall, understand and apply essential knowledge without outside assistance.

The bigger danger is when faculty members do not understand the difference between a final examination and an alternative final assessment.

A lecturer may take the existing final examination paper, upload it into the LMS, give students one or two days to complete it at home, and rename it “Final Assessment”.

That is not assessment transformation.

It is simply a take-home examination with weaker controls.

The institution may lose the security of a supervised examination without gaining the benefits of authentic assessment. Students can discuss answers, share files, use generative AI, engage third-party assistance or submit work that does not represent their own ability.

The result is the worst of both worlds:

• It is not a secure final examination.
• It is not an authentic alternative assessment.
• It may not provide reliable evidence of individual learning.
• It increases academic integrity risks.
• It can contribute to grade inflation.
• It may weaken confidence in the academic award.

Another risk is forcing lecturers to redesign assessments before lecturers have sufficient assessment literacy.

When alternatives are imposed without proper understanding, lecturers may simply replace a three-hour examination with another written assignment. Students then complete several similar reports in different courses, while lecturers face a much heavier marking workload.

Changing the name does not change the quality.

A weak assessment remains a weak assessment, regardless of whether we call it an examination, assignment or final assessment.

The issue is not whether examinations are traditional or alternative assessments are modern. The real issue is whether the selected method is valid, reliable, fair and suitable for the intended learning outcomes.

In the final article, I will share a more responsible way to make the transition.

#HigherEducation #AssessmentDesign #AcademicIntegrity #QualityAssurance #GenerativeAI

Go to the previous article: The Excitement of Alternative Final Assessment - Higher Education Perspectives

The Excitement of Alternative Final Assessment - Higher Education Perspectives

For many years, the sit-in final examination was treated as the default way to assess university students.

Students entered an examination hall, answered a set of questions within two or three hours, and left. It was controlled, familiar and relatively easy to manage.

But there was always one important question:

Does a final examination truly show what a student can do?

A student may be able to memorise theories and reproduce them during an examination. But can the student apply those theories, solve a real problem, create a product, defend a decision or perform a professional task?

This is where alternative final assessment became attractive.

National higher education quality guidance recognises that a final assessment can take different forms. It may be a final examination, but it may also be a final project, portfolio, exhibition, reflective assessment, seminar, expert review, critique or logbook.

This gives higher education institutions more flexibility to assess learning in ways that are more authentic and relevant.

For example:

• A learning design student can develop an online learning module.
• A business student can analyse a real organisational problem.
• A computing student can build and demonstrate a working system.
• A communication student can produce a digital campaign.
• An education student can develop and defend a teaching portfolio.

The intention is good. Instead of only asking students what they know, we can ask them to demonstrate what they can do.

However, one important distinction must be understood:

Final Assessment does not mean Final Examination has been abolished.

A final examination is still one form of final assessment. Alternative assessment is another.

Source: Guidelines to Good Practices: Assessment of Student Learning. Malaysian Qualifications Agency 2023
https://www2.mqa.gov.my/qad/v2/2024/DOKUMEN%20GGP%20AOSL%209223%20FINAL%20V3.pdf 

The flexibility was introduced to allow institutions to select the most appropriate assessment method for the learning outcomes. It was not introduced to force every course to abandon examinations.

That difference is extremely important.

In the next article, I will discuss what can go wrong when an institution becomes too excited and attempts to convert every final examination into an alternative final assessment.

Go to next article: The Danger of Going All Out - Alternative Final Assessment in Higher Education

#HigherEducation #Assessment #AuthenticAssessment #AcademicQuality #TeachingAndLearning