​​Guidance for Teaching and Learning with Generative Artificial Intelligence​

​Version 1: January 20, 2026​

This document has been developed by QTL_net in consultation with AKU faculty and staff to support faculty and others involved in teaching undergraduate, graduate, and post-graduate programmes at the Aga Khan University (AKU). Its purpose is to help them make informed, ethical, and pedagogically sound decisions about whether and how to use Generative Artificial Intelligence (GenAI) in teaching, learning, and assessment.

This guidance complements the Guidelines on the Use of Generative AI in Higher Education at AKU and builds on the earlier QTL Guidelines for Faculty and Teaching Assistants on using Generative AI tools in Teaching and Learning​, developed in May 2023. Collectively, these documents are designed to assist faculty in translating institutional guidelines into teaching and assessment practice.

We recognise that approaches to GenAI will vary across disciplines and that no single approach is suitable for all courses or contexts. Instead, the emphasis is on setting clear expectations, aligning with learning outcomes, maintaining academic integrity, and ensuring responsible human oversight. The overarching principle is that GenAI should be used to enhance learning, uphold academic integrity, and promote equitable access. It must not replace human judgement, responsibility, or scholarly engagement. Faculty expertise, professional judgement, and ethical oversight remain central to teaching and assessment in an AI-aware university environment.

1. Purposeful Use of GenAI in Teaching​

  • The use of GenAI tools in teaching is at the discretion of the faculty, unless otherwise specified by programme, accreditation, or curriculum requirements. However, this discretion does not remove the need to explicitly address student use of GenAI within the course.

  • Faculty must clearly communicate expectations regarding GenAI use, including where GenAI use is permitted, limited, or prohibited for students, through:

    • Course syllabi or handbooks​​

    • Assignment and assessment instructions

    • Classroom discussions, particularly at the start of the course and before major assessments

  • Expectations should explicitly address:

    • Faculty use of GenAI (e.g., for preparing teaching materials or assessments)

    • Permitted, limited, or prohibited student use of GenAI in learning activities and assessments

  • Where relevant, faculty are encouraged to situate GenAI use within disciplinary or professional contexts, including its benefits, limitations, risks, and ethical implications.

  • Faculty should maintain ongoing dialogue with students about GenAI, learning expectations, and academic integrity, recognising that clarity and transparency are essential in environments where GenAI tools are widely available.​

  • AKU currently provides access to Microsoft Copilot as a university-approved GenAI tool. Data shared within Copilot is processed within Microsoft’s enterprise environment in accordance with institutional agreements and is not used to train public AI models or made accessible to other users. However, faculty and students are still advised not to upload confidential, sensitive, or classified information.​​​


2. Selection and Adoption of GenAI Tools in Teaching and Learning

  • GenAI tools should be used based on alignment with course learning outcomes and disciplinary or professional relevance.
  • Preference should be given to:
    • Institutionally approved tools (e.g., Microsoft Copilot)
    • Tools that are intuitive, accessible, and suitable for a diverse student population
  • Faculty should avoid tools that:
    • Require students to incur additional costs
    • Create access barriers or inequitable learning conditions
    • Breach institutional or national data protection requirements
  • Accessibility considerations are essential, including support for diverse linguistic, cultural, and cognitive needs.
  • Faculty should balance the number and complexity of GenAI tools with other educational technologies to avoid cognitive overload.
  • When there is uncertainty about the technical functionality, security, or data-handling practices of a GenAI tool, faculty should consult with the IT department before recommending it for teaching or assessment.

3. Pedagogical Considerations

  • Faculty are encouraged to use GenAI to foster critical thinking by engaging students in evaluating accuracy, bias, limitations, and ethical implications of AI-generated content.
  • Instructional approaches should demonstrate how learning outcomes can be achieved more effectively with thoughtful GenAI support than without it.
  • Where appropriate and supported, GenAI tools may be used to personalise learning experiences or adjust levels of challenge.
  • GenAI should be understood as augmenting, not replacing, sound pedagogy, disciplinary expertise, and professional judgement.
  • Students who prefer not to use GenAI for ethical, cultural, or personal reasons should be offered reasonable alternatives where feasible.​

4. Assignments, Assessment, and Feedback

  • Assessments should prioritise human-centred learning processes such as reasoning, analysis, synthesis, reflection, and decision-making, rather than focusing solely on final outputs. Ultimately, assessment design should support the development and demonstration of graduate attributes.
  • Grading criteria and rubrics should be designed to evaluate critical engagement, originality of thought, and evidence of learning.
  • Approved GenAI tools (e.g., Microsoft Copilot) may be used to support feedback processes; however:
    • Faculty and teaching assistants remain fully responsible for grading and academic judgement
    • GenAI must not be the sole source of feedback or assessment decisions
  • Faculty must clearly state whether GenAI-generated media (e.g., text, images, audio, video) are permitted and how such materials should be evaluated, credited, and referenced. Any GenAI-generated content included in graduate dissertations or theses must be disclosed and properly referenced, including the prompts used.
  • Although AI-detection tools exist, faculty are reminded that none of them has yet guaranteed accurate detection. Therefore, faculty are strongly advised to consider the academic and mental repercussions of false accusations on students for the unethical use of AI tools.
  • Where GenAI use is permitted, students may be required to retain records of their AI interactions (e.g., prompts and outputs) to support transparency and academic integrity.
  • In unsupervised assessments (e.g., out-of-class quiz, written work), discuss with students the productive and responsible use of AI tools. Consider alternate assessments, such as flipped assessments in which students solve the problem using GenAI and assess the outcome, or adding an oral component to a written task.
  • Some examples of secure assessments designed to limit the feasibility of GenAI assistance through supervision, time constraints, and assessment design include:
    • In-class tests or examinations that emphasize application, reasoning, or process, conducted under supervised and time-bounded conditions
    • Oral presentations, vivas, interviews, and debates that require real-time explanation, interaction, and defence of ideas
    • Supervised reflective work based on in-class activities, writing, or problem-solving tasks completed during class or designated sessions
    • Practical, laboratory, clinical, OSCE, Simulation, or performance-based assessments where competence is demonstrated through action and decision-making

5. Use of GenAI in Thesis, Dissertations, or Capstone Projects

  • The supervisor and student must discuss the use of GenAI at the start of the research or project process. What is acceptable and what is unacceptable must be agreed in advance by the student and supervisor, and, where applicable, by the supervisory committee, before the student begins the research or project. The following table 1 provides examples of how faculty may allow students to use GenAI in research:

Table 1: Use of GenAI in student research

Research phase​​​AllowedResponsibility
Literature review​​​

  • Summarising or comparing academic articles
  • Identifying themes or gaps in the literature
  • Organising notes or early drafts

     

Students remain responsible for verifying sources, checking accuracy, and identifying bias

 

Data Analysis
  • Supporting exploratory analysis
  • Explaining methods
  • Assisting with troubleshooting code ​or interpretation

Students must:

  • Not upload raw data including sensitive, confidential, or participant data to publicly available GenAI tools
  • Disclose the use of GenAI in the methodology section of their proposal and dissertation
  • Clearly explain how GenAI was used
  • Verify and validate all GenAI outputs , retaining full responsibility for accuracy, integrity, and originality
Writing
  • Improving clarity, grammar, and structure
  • Assisting with developing an outline

The core ideas, arguments, analysis, and interpretation must be the student's own work

 

 

  • Faculty should clearly inform students that the following uses of GenAI are not acceptable:
    • Submitting GenAI-generated text as original work without acknowledgment
    • Using GenAI to replace critical thinking, analysis, or interpretation
    • Uploading raw data, including confidential, sensitive, or participant data, to publicly available GenAI tools
    • Listing GenAI tools as authors or co-authors
    • Using GenAI in peer review where it is not explicitly permitted
    • Using GenAI in ways that breach copyright, intellectual property, or research ethics
  • Faculty should require students to:
    • Acknowledge any use of GenAI in the​ses, dissertations, or major research outputs
    • Clearly state how and for what purpose GenAI was used
    • Be prepared to explain and defend their work orally, for example, in supervision meetings or a viva​
  • Faculty should advise students to seek ethics guidance when GenAI is expected to be used with participant data and encourage early consultation with the Ethics Committee where appropriate.

6. Academic Integrity and Responsible GenAI Use

  • Academic integrity should be approached holistically, recognising that unclear expectations, workload pressures, or limited GenAI literacy may contribute to inappropriate use.
  • Faculty must clearly define when, how, and to what extent GenAI tools may be used for each assignment or assessment.
  • When GenAI use is not permitted:
    • Faculty should review and, where necessary, redesign assessments to ensure learning outcomes are not compromised
    • Appropriate strategies may include in-class assessments, staged or iterative submissions, reflective writing, oral explanations, or supervised tasks
  • When GenAI use is permitted:
    • Students remain fully responsible for the accuracy, originality, and integrity of their work
    • Students must verify all GenAI-generated content and apply critical judgment
  • In cases of suspected misuse, faculty should first discuss the matter with the student to clarify expectations about GenAI use, the student's use, and intent before initiating formal academic integrity processes. This helps avoid premature accusations and potential harm to the student. Please refer to guidance on responding to suspected GenAI misuse, including How to Respond if You Suspect an AI Tool Wrote an Assignment.

7. Limitations of GenAI Detection Tools

  • Publicly available detection tools are unreliable and may produce biased or inaccurate results, with potential academic and psychological consequences for students. Thus, GenAI detection tools must not be used as the sole or primary basis for grading decisions or determinations of academic misconduct.
  • Turnitin is used by the Office of Graduate Programme Administration as a text-matching and similarity-detection tool for academic integrity. All graduate-level theses and dissertations are uploaded to Turnitin via AKU's institutional repository, and this submission is a mandatory institutional requirement. The similarity report generated by Turnitin serves as a support tool for academic review and, on its own, does not constitute a determination of plagiarism. Academic judgment rests with the supervising faculty and relevant academic committees.

8. Privacy, Data Protection, and Consent

  • Faculty must ensure that any GenAI tools used in teaching comply with institutional policies related to privacy, data protection, intellectual property, and national data protection laws.
  • Students must be clearly informed when their data or work will be processed by GenAI systems. Uploading student work into external detection tools without explicit consent may violate privacy, data protection, and copyright obligations.
  • Data shared with GenAI systems should be limited to what is strictly necessary. Sensitive, confidential, or identifiable information must not be entered unless formally approved. Faculty should clearly communicate that raw data, datasets, synopses, and other project research materials must not be uploaded to publicly available GenAI systems for analysis, as doing so may compromise data ownership, confidentiality, or intellectual property.
  • Faculty should remind students that teaching materials, peer work, and assessment content must not be uploaded into GenAI tools without permission.

9. Review of the guidelines

  • This is expected to be a living document and will be updated regularly.
  • A light-touch review will be conducted six months after these guidelines are introduced, or earlier if required, to identify any immediate gaps, risks, or unintended consequences arising from the use of GenAI in teaching and assessment. This review will be informed by structured feedback and evidence, including input from faculty and students gathered through surveys and/or in-depth discussions.
  • Thereafter, the guidelines will be reviewed and updated annually or sooner if significant technological, regulatory, or institutional changes occur, to ensure they remain aligned with new AI developments and relevant regulatory requirements.
  • Please use this form to provide feedback or to share your experiences with these guidelines.

10. Resources and Institutional Support

  • AI models and AI-powered tools are constantly improving. Therefore, it is important for faculty to continue to upgrade their knowledge and understanding of their new capabilities and how these might affect teaching and assessment. Faculty are encouraged to seek guidance, resources, and professional development related to GenAI through the Network of Quality, Teaching, and Learning (QTL_net). Contact: aku.aiep@aku.edu
  • Support related to copyright, intellectual property, and referencing should be sought from the Library. Contact: copyright@aku.edu
  • The ICT Department oversees data protection, tool approval, and licensing for GenAI use. Contact: it.servicedesk@aku.edu
  • The Office of Graduate Programme Administration supports graduate programmes in reviewing dissertations and theses in accordance with these guidelines and will assist them in ensuring the ethical use of AI in line with University and national policies. Contact: husain.azfar@aku.edu
  • Relevant University policies:
  • Other resources:

Click here to download the PDF.