Recent research (Digital Education Council & Pearson, 2025) suggests that assessments now need to evolve both to protect academic integrity and to prepare students for working and learning in a GenAI-enabled world. This shift requires moving beyond a singular focus on “How do I stop my students from using AI?“ toward a more nuanced set of questions, such as:
- How can GenAI be used to enrich learning and support student choice?
- Which of my assessments remain meaningful in an GenAI-rich environment?
- How do I balance essential human skills such as originality, analysis, and ethical judgment with GenAI literacy and awareness?
Guiding principles for integrating AI in assessment
These evolving guiding principles outline how to integrate AI into assessment at UBC in ways that uphold transparency, protect student privacy, support equity of access, and promote ethical, responsible use.
- Be transparent and contextualize AI policy
- As noted in the “Communicating with students” section of this resource, it is important to clearly communicate when and how AI tools may or may not be used in each assessment, both in syllabi and assignment instructions. Explain to students what is allowed, what is discouraged, and provide context for why those decisions are made.
- Protect privacy
- GenAI tools can pose considerable privacy and data security risks. Instructors can only require students to use AI (or other technology tools) that have passed a Privacy Impact Assessment (PIA) process. Refer to the PIA for GenAI Instructional Use web page for which tools have been reviewed for instructional purposes. Tools requiring personal data can be recommended as optional so long as an equivalent alternative is available for those who do not wish to provide their personal information. Remind students of guidance for all: avoid entering personal or sensitive information into AI tools, and never share your UBC credentials with anyone, including non-UBC AI tools or agents.
- For further information, review the UBC Privacy Matters PIA Guidelines on AI tools and UBC’s Generative AI in Teaching and Learning Guidelines.
- Scaffold GenAI literacy
- Support students’ varying familiarity with AI. Where possible, provide examples, short training on prompting, and strategies to evaluate outputs for errors and bias. Highlight differences between free and paid tools, and model effective, critical use.
- Support equity of access
- To reduce barriers, ask students to use the same free or low-cost tool where possible. Required tools must have passed UBC’s PIA (e.g., Microsoft Co-pilot, available for free at UBC). Encourage reflection on how access to different tools may shape learning outcomes.
- Promote ethical and responsible use
- Foster reflection on AI’s broader impacts through brief discussions, prompts, or activities. Key areas include:
- Accuracy: Outputs may be unreliable; verify and cross-check.
- Bias: Data may reinforce stereotypes; question and critique.
- Intellectual property: Cite AI contributions transparently.
- Privacy: Avoid entering sensitive information.
- Environmental impact: Recognize the energy demands of large-scale AI.
- Refer to the UBC Guidelines for All Uses of GenAI in Teaching and Learning for further guidance.
- Foster reflection on AI’s broader impacts through brief discussions, prompts, or activities. Key areas include:
- Highlight human-AI collaboration and the learning process
- Leverage GenAI to support creativity and idea generation while ensuring students remain the primary drivers of their work. For example, you might permit student to use AI for some preliminary aspects of assignments (such as brainstorming, initial information gathering, or generating outlines), but require them to do their own work in later steps of the process. You could also ask students to demonstrate their learning journey, whether by showing intermediate steps, documenting decisions, or reflecting on how AI informed their final work.
Strategies for integrating GenAI in assessment and assignment design
GenAI can be used in different ways across assessments and assignments. To help guide your design choices, the Digital Education Council and Pearson describe three categories of AI use:
- GenAI-Free: Assignments are designed to minimize or prevent GenAI use, ensuring work is primarily student-generated. Review the section of this resource on AI resilient assessments for sample approaches.
- GenAI-Assisted: Students use GenAI as a support tool at specific stages of the learning process (e.g., brainstorming or editing), but must critically evaluate and adapt the outputs.
- GenAI-Integrated: GenAI is intentionally embedded as a core component of the task, requiring students to demonstrate advanced collaboration with GenAI alongside disciplinary knowledge.
This framework provides a spectrum of options, helping you balance integrity, equity, and innovation while aligning assessment with course learning goals.
Approaches and examples of integrating GenAI in assignments and assessments
The examples below illustrate strategies for meaningful integration of GenAI into assessments and assignments, including those that might be considered “AI-Assisted” or “AI-Integrated.”
Students evaluate and revise GenAI output
GenAI tools often produce outputs that contain inaccuracies, hallucinations, or biases. Rather than treating these flaws as a weakness, assignments can leverage them as opportunities for learning. By generating and analyzing GenAI outputs, students can identify errors, biases, and different perspectives, and then work on revising these outputs to either improve them or reflect on the errors detected. This practice not only leverages GenAI limitations but also aids in developing evaluative judgment — an essential component of critical thinking, especially important given the growing reliance on GenAI.
UBC Example – Anubhav Pratap-Singh: FNH 303
FNH 303 (Food Product Development), Dr. Pratap-Singh has modified the traditional “Product Selection” part of the assignment for New Product Development, allowing and encouraging students to use ChatGPT to identify recipes (ingredients and their compositions) for new food product development. Students are expected to use the Internet to modify an existing product in the market to either: a) make it more sustainable by removing problematic ingredients/processes; or b) modify the product to either add a claim (for example gluten-free; plant-based; etc.). Dr. Pratap-Singh is encouraging students to seek replacement ingredients using ChatGPT, as well as use other Internet sources to describe known chemical and physical properties of different ingredients in the food product under consideration. To demonstrate their understanding of how each ingredient works, students are expected to address how modifications of the formulations may lead to food products with different traits (sweeter, chewier, etc.).
GenAI for critical analysis and documentation
Incorporating GenAI as part of ethical inquiry or case analysis encourages students to reflect on its role in professional and research contexts. Students can be tasked with documenting their GenAI use, evaluating outputs against scholarly sources, and reflecting on the limitations and ethical implications.
UBC Example – Jared Taylor MICB 418: Industrial Microbiology
In MICB 418: Industrial Microbiology, Jared Taylor encourages students to responsibly utilize GenAI tools for their final group presentations on ethical and legal issues within industrial microbiology. Students must critically evaluate and verify the accuracy of the information provided by these GenAI tools, and they are required to document their usage of the tools throughout their project development. To support this, Dr. Taylor provides an academic integrity checklist and a reflective survey, developed with Dr. Simon Bates, which students must complete and submit. This process not only ensures adherence to academic standards but also promotes deep reflection on the role and reliability of GenAI in scientific research and presentation.
GenAI-supported reflection and personalization
Assignments can invite students to use GenAI to generate study strategies, approaches to problem-solving, or alternative perspectives on a topic, followed by reflection on the usefulness, limitations, and accuracy of the outputs. This process supports metacognition, encourages critical evaluation, and acknowledges students’ diverse needs and contexts, aligning with UBC’s commitment to equity and accessibility.
UBC example – Kari Grain, ADHE 328: Social Institutions of Adult Learning
In a course on contemporary changes in adult education, students use ChatGPT to analyze recent developments in the field. Based on Chuang’s 2021 article, which highlights areas such as the growth of technology-based learning, increased cross-cultural training, and a stronger focus on adult learner characteristics, students are tasked with exploring these themes. They access ChatGPT and input a personalized prompt asking for specific ways they can learn best as adults, considering their preferred method of learning (e.g., listening, playing, doing, imagining). Students then paste ChatGPT’s response into their discussion posts and evaluate its usefulness and accuracy, while also reflecting on potential issues with relying on GenAI for research-based information. This exercise not only enhances their understanding of adult learning but also demonstrates the practical application of GenAI tools in educational settings.
Brainstorming, outlining and ideation
GenAI can be a useful tool for ideation, outlining and brainstorming, and faculty are increasingly incorporating GenAI as part of the thinking or writing process.
One of the primary ways GenAI is being utilized in courses is to assist students in brainstorming and generating ideas. GenAI can be prompted with a topic or concept, and it can generate a list of related ideas, topics, or questions to explore. This can help students overcome writer’s block and expand their thinking, reasoning beyond their initial thoughts. GenAI can also be used to create mind maps or visual representations of ideas, aiding in the organization and structuring of thoughts.
In addition to brainstorming, GenAI is being used to draft aspects of students’ writing where this fits with the learning goals of the course. For example, students can use GenAI to generate topic sentences, outlines, or even complete paragraphs that students then revise. This can save time and provide a starting point for students who may struggle with getting their thoughts on paper. GenAI can also be used to generate different versions of a sentence or paragraph, allowing students to compare and provide ideas for improving their own writing.
UBC example – Kari Grain, ADHE 328: Social Institutions of Adult Learning
In a creative exercise inspired by Stivers and Lehrman (2023), students explored the future of adult education through AI-generated art on Padlet. They envisioned their ideal learning environments, considering key aspects of adult learning discussed in class. After describing their visions on Padlet, the GenAI tool generated six versions of their environments, from which students selected their favorite. They then explained the symbolism and connection to course readings. This exercise demonstrated the creative use of GenAI in education, allowing students to engage with the material in an innovative way while exploring the potential of GenAI tools in teaching and learning.
GenAI as continuous assistant throughout the assignment
UBC example – Qingshi Tu, Industrial Ecology/Sustainability Engineering.
As part of the introductory Python lecture, students were tasked with creating a Python script to simulate the 649 lottery. They were encouraged to utilize ChatGPT to enhance their learning experience by: (1) asking it to explain the mechanism of the 649 lottery, and (2) generating the Python code required for the simulation. Students then tested the generated code in Google Colab, using ChatGPT further to debug and refine their scripts. This exercise not only helped students understand the lottery mechanism and Python programming but also demonstrated the practical use of GenAI tools in coding and debugging.