Explore Brightspace for Higher Education
See how Brightspace supports teaching, learning and student success as institutions adapt to AI
AI in higher education presents institutions with several challenges: deciding where the technology improves teaching and learning, where it introduces risks and how to use it responsibly.
This guide helps simplify those decisions by looking at how artificial intelligence is changing teaching, learning, assessment and student support.
You’ll see where AI can add value, the risks to consider and the steps your institution can take to implement it responsibly while keeping faculty judgment or the quality of student learning.
How Is AI Changing Higher Education?
For years, AI seemed like a technology of the future, often associated with robots and complex machines. Today, generative AI tools are accessible through the phones and laptops we use every day.
That easy access has brought AI into everyday work and study, fueling demand for tools that can do more and, in turn, pushing their capabilities forward at a rapid pace.
As these AI capabilities grow and more higher education institutions incorporate AI into their learning platforms and existing teaching and learning workflows, it changes:
- How much you can personalize learning to individual learner needs
- How faculty create course content and provide feedback
- What assessments can reliably demonstrate about student learning
- How quickly your teams can identify and respond to learner needs
Below, we look at how these changes are taking shape across higher education.
Personalized Learning and Student Support
One of the first places you may see a difference when bringing AI into teaching and learning is personalization. Think about a course with 300 students. Traditionally, those students might receive the same set of study materials and move through the course at roughly the same pace, while you face the difficult job of keeping track of how each learner is progressing.
With AI, you have more ways to respond to those differences at scale. Adaptive learning tools use learner performance and activity to tailor study support and recommend resources based on where each student needs more help.
AI tutors and chatbots give students additional ways to get support, while learner signals help your faculty and student support teams identify who may need attention without manually reviewing the activity of all 300 students.
D2L Lumi shows what this looks like within the learning environment. After a quiz, D2L Lumi Study Support uses a learner’s performance to recommend content that helps them spend more time on areas where they need practice.
When they need help while studying, D2L umi Tutor provides support grounded in their course content, so the learning experience is in sync with what they are working on and where they need help.
Course Design and Teaching
Developing a course involves a lot of groundwork before anything reaches a student, from drafting activities and discussion prompts to creating assessment questions and aligning materials with learning outcomes.
Generative AI helps shorten parts of that process by giving faculty and instructional designers a starting point to work from. For example, D2L Lumi Idea generates assignment and discussion ideas from course content, D2L Lumi Quiz turns that content into questions and D2L Lumi Outcomes helps align materials and assignments with learning outcomes.
This changes where your teams spend their time. Rather than starting every activity or question from a blank page, they can spend more time reviewing, adapting and improving what was generated for their students.
| 💡Faculty judgment remains essential throughout the process. |
Assessment and Feedback
The impact of AI on course design naturally carries through to assessment in more than one way.
For faculty, the change starts before an assessment even reaches a student and continues after they submit it. AI tools help generate questions and draft personalized feedback, reducing time spent on repetitive parts of the process while leaving the instructor responsible for reviewing the output, evaluating student performance and deciding what feedback to provide.
AI is also changing how students approach their coursework. A survey of more than 8,000 students across four Australian universities found that over 80% had used generative AI for study-related tasks and nearly half used it regularly, primarily for editing, summarizing and generating ideas.
When AI is involved at different points in a student’s work, a submitted assignment may no longer accurately reflect what they can do independently.
This broadens the conversation around AI and assessment beyond detecting AI-generated work. Institutions need to set clear expectations for acceptable AI use and develop innovative assessment practices that provide credible evidence of what students know and can do.
Learning Analytics and Academic Advising
Beyond these changes to teaching and assessment, AI also changes what you can do with the learner data your institution already collects in the following ways:
1) Spot potential support needs earlier
Predictive analytics can surface patterns in engagement and performance that may not be obvious from periodic grades alone or until a student asks for help.
2) Prioritize outreach
Your faculty, academic advising and student success teams can use those signals to identify where a check-in may be useful, rather than manually reviewing every learner’s activity.
D2L Performance+ supports earlier intervention by helping your teams identify patterns in learner performance. Its Student Success System uses predictive analytics to generate a weekly success index, giving educators another signal for identifying students who may need support.
That signal should start a conversation, not make a decision. Your team should review what is happening with the student, speak with them and use that context alongside their professional judgment to decide what support is appropriate.
Benefits of AI in Higher Education
The benefits of AI extend across teaching, learning and student support, particularly in areas where educators need to respond to individual learner needs at scale.
They are:
1) Give students more personalized support
AI-powered tutors and adaptive learning tools provide students with additional support based on their learning needs. Students can ask questions, revisit difficult concepts and access relevant learning resources without waiting for their next interaction with an instructor.
2) Reduce repetitive faculty work
AI helps educators spend less time on routine tasks such as drafting assessment questions, preparing course materials and generating initial feedback. This gives faculty more time to review student work, improve instruction and provide individual support.
3) Provide feedback sooner and at scale
AI-assisted feedback tools help educators generate feedback on student work more efficiently, particularly in large courses. With faculty reviewing the suggestions before students receive them, institutions can improve feedback turnaround times while maintaining academic oversight.
4) Create richer learning experiences more efficiently
Generative AI helps faculty and instructional designers develop learning activities, discussion prompts and interactive materials. Educators can use these outputs as starting points, then adapt them to their course objectives and students’ needs.
5) Give students more ways to engage with content
AI-supported learning tools can help make course materials available in different formats, including summaries, study guides and interactive activities. This gives students more ways to engage with information based on their learning preferences and support needs.
6) Turn learner activity into useful insights
AI-powered learning analytics help institutions identify patterns in student engagement and performance. Faculty and student support teams can use these insights to identify learners who may need additional attention and make informed decisions about where to intervene.
AI in Practice: How The Independent Institute of Education Uses Brightspace
The challenges The Independent Institute of Education (IIE) faced may sound familiar. With thousands of modules and tens of thousands of students, the institution wanted to give learners more personalized support while reducing the repetitive work taking up lecturers’ time.
To achieve that, the IIE introduced D2L Brightspace™, an AI-enabled learning management system (LMS), alongside Creator+, Performance+ and D2L Lumi to:
- Provide personalized student support
- Deliver timely feedback
- Create interactive learning experiences
- Gain insights into student engagement
The IIE also redesigned its learning modules to give students more flexibility in how they access content, including videos with captions and transcripts.
After a six-week pilot with 2,500 students, the IIE expanded its use of Brightspace to 65,000 students within a year, demonstrating how an institution can scale AI-supported learning at scale.
Risks and Considerations of AI in Higher Education
In the 2026 HEPI Student Generative AI Survey, 49% of students said AI had improved their student experience, citing benefits such as saving time, improving understanding and getting immediate support.
At the same time, students raised concerns about fairness, losing important skills, social isolation and what AI could mean for future employment.
Below are some of the risks and considerations to account for as you introduce AI across teaching, learning and student support:
| Area | Risk or Consideration |
| Personalized learning and student support | AI may recommend content or support based on signals such as quiz performance and course activity without knowing why a student is struggling. This can result in recommendations that do not match the student’s actual learning needs, highlighting the importance of the human element in AI-supported higher education. |
| Course design and teaching | Using AI to generate course structures, activities or materials can produce a learning experience that is poorly sequenced or mismatched with how students need to progress through the course. Generated content can also contain inaccuracies or fail to support the intended learning outcomes. |
| Assessment and feedback | When students use generative AI to complete assessed work, the final submission may not accurately show what they know or can do independently, creating academic integrity and assessment validity concerns. AI-generated grading or feedback may overlook context, originality or discipline-specific reasoning in a student’s work. This is because AI may rely on predefined criteria or patterns that do not account for how different subjects and assessment types demonstrate learning, leading to inaccurate evaluations or feedback. |
| Learning analytics and advising | Predictive analytics can flag students as needing support based on patterns in their performance and activity, but those patterns may not explain what is actually happening. Misinterpreting AI analysis of student behavior could lead to unnecessary interventions or cause students who need help to be overlooked. Collecting and analyzing student activity and performance data for these predictions also raises questions about data privacy. |
| Institutional adoption | As AI use expands across your institution, different departments may adopt tools with different standards for security, privacy, accessibility and acceptable use. Without consistent oversight, this can expose student or institutional data, create uneven experiences across courses and make accountability unclear when problems occur. |
How to Implement AI in Higher Education Responsibly
Responsible AI adoption requires more than choosing where the technology could be useful. Your institution also needs the structures to guide how it is introduced, used and evaluated over time.
The steps below outline the structures you need to put in place and the actions you need to take to implement AI responsibly across your institution.
Establish Clear AI Policies and Governance
AI adoption often moves faster than awareness of the guidance around it. A survey by EDUCAUSE found that while 94% of higher education respondents had used AI for work, only 54% were aware of institutional policies or guidelines governing its use.
This gap shows why your institution needs to clearly define and communicate its approach to AI as AI adoption in higher education grows.
Set institution-wide expectations for permitted and prohibited uses, responsibility for AI-assisted work, data requirements and who owns governance decisions.
For assessments, give teachers room to define appropriate AI use based on their courses and learning outcomes. Then, review your guidance at least annually to keep pace with changes in AI, and revisit it sooner when new technology, regulations or institutional practices affect how your institution uses AI.
Build Faculty AI Literacy
Clear policies give faculty boundaries, but they also need the skills to make informed decisions about when and how to use AI in teaching and learning.
Build faculty development around the decisions educators need to make when using AI, including how to evaluate the quality and accuracy of AI-generated content, determine when AI is appropriate for teaching and learning and handle student or institutional data responsibly.
Make the training practical and relevant to the work the teaching faculty already do. Let educators test AI tools using examples from their own disciplines, then evaluate the results and discuss where AI fits into teaching and learning.
Use these sessions to explore the ethical concerns of AI in education, and where educators need to apply their own judgment. This approach gives teachers and learners experience they can carry into their courses instead of generic AI training.
Redesign Assessment for AI Use
With AI already in students’ hands, your assessments need to show what students actually know and can do, even when they use AI as part of their work. Be explicit about when AI is allowed, how students can use it and what they need to complete independently.
Look beyond the final submission, too. Ask students to show how they reached an answer, apply what they know to a specific problem and explain the reasoning behind their decisions. When they use AI, you can also ask them to document or reflect on how they used and evaluated its output.
This approach helps students demonstrate their understanding, strengthen their critical thinking skills and reflect on how they use AI. It also gives educators clearer evidence of student learning and helps them identify where additional support may be needed.
Evaluate AI Tools and Their Impact
Evaluate every platform’s reliability, data privacy and security, accessibility, potential bias, compatibility with existing systems and requirements for human oversight.
Without this review, your institution risks adopting AI tools that don’t meet its standards for privacy, security, accessibility or accuracy. For tools that meet those requirements, run a pilot before wider adoption and define success based on the need you started with.
For example, you might evaluate an AI feedback tool based on the quality and timeliness of its feedback or an early-intervention tool based on whether it helps your teams support students sooner.
Combine these measures with faculty and student feedback, then use the results to expand, adjust or discontinue the tool.
Preparing Students for an AI-Enabled Future
As AI becomes part of professional work, workforce readiness increasingly includes knowing how to work with it. Your students need opportunities to learn when AI is useful, how to evaluate its output and when their own expertise and judgment should take precedence.
There is already a gap between that need and students’ preparation. While 68% of students say AI skills are essential to thrive in today’s world, only 48% say their teaching staff are helping them develop those skills for their future careers.
Closing that gap means bringing AI literacy into students’ disciplines. An accountant, teacher and engineer will use AI differently, so preparation should reflect the tools, expectations and limitations they will encounter in their respective fields.
Brightspace gives you a learning platform for adapting teaching, assessment and learning experiences as students prepare for careers where AI is part of everyday work.
With the right tools and learning opportunities, your institution can help students develop the skills and judgment to use AI confidently and responsibly, both during their studies and beyond graduation.
Frequently Asked Questions About AI in Higher Education
What Is the Role of AI in Higher Education?
AI has no fixed role in higher education. Each institution determines how to use it based on its teaching goals, student needs and priorities. While AI can support personalized learning, course development, assessment and student support, it should complement rather than replace faculty expertise, human judgment and meaningful interactions between educators and students
How Are Universities Using ChatGPT and Generative AI?
Universities may use ChatGPT and generative AI to develop course materials, generate ideas, support research and assist student learning. Institutions should also establish clear guidelines for acceptable use, disclosure and academic integrity to help faculty and students use these tools responsibly.
What Is the Difference Between Generative AI and Adaptive Learning in Higher Education?
Generative AI creates content such as text, questions and feedback. Adaptive learning uses learner performance and activity to personalize support or recommend resources based on individual learning needs.
How Can Higher Education Institutions Address Digital Equity When Using AI?
Institutions can support digital equity among students and faculty by ensuring access to devices, reliable internet connectivity and AI tools. They should also evaluate accessibility and avoid requiring technologies that some students or educators cannot reasonably access or use.
How Does AI Affect Competency-Based Education?
AI can support competency-based education through personalized learning recommendations, timely feedback and insights into student progress. Educators still need reliable assessments and professional judgment to determine whether students have demonstrated the required competencies.
What Should Universities Look for When Choosing AI Tools for Higher Education?
Evaluate AI tools for reliability, data privacy, security, accessibility, potential bias and compatibility with existing systems. Institutions should also consider the level of human oversight required and whether the tool addresses a genuine teaching, learning or student-support need.
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