In higher education, AI (artificial intelligence) is ushering in both promising advancements and complex challenges, especially in the pursuit of inclusion on college campuses.
According to the 2024 AI Landscape Study released by Educause — a nonprofit association whose focus is on advancing the use of technology and data to impact success in higher education — colleges are increasingly integrating AI to enhance student experiences, improve administrative efficiency, and influence teaching methodologies.
However, the report reveals a landscape marked by cautious optimism, with stakeholders wary of the ethical and operational risks AI brings, particularly around academic integrity, data privacy, and equity.
Advancements and Opportunities
Utilization of AI offers institutions unprecedented potential to tailor educational experiences, which could be instrumental in advancing inclusive practices. By personalizing learning and providing adaptive support, this technology can help students with diverse backgrounds and learning needs succeed.
For example, AI-powered tools can provide real-time tutoring, language translation, and academic advising, technologies that have the potential to reduce resource disparities and create more equitable learning environments, according to studies at the Deloitte Center for Government Insights — a research-focused arm of the global business consulting firm, Deloitte.
“The advent of generative AI tools in education allows for a more inclusive approach where students can engage with material in a way that best suits their learning styles, significantly broadening the likelihood of academic success,” the researchers, led by higher education innovator Tamara Askew, principal at Deloitte, wrote in a recent policy brief.
In addition, colleges and universities are increasingly exploring how AI can streamline administrative tasks and support faculty efforts to create more accessible course materials. AI tools, such as those that assist in automating tasks or performing data analytics, also enable staff to focus on more personalized engagement with students.
“Integrating advanced language models into learning systems can considerably facilitate the incorporation of Universal Design for Learning principles,” Deloitte researchers wrote. “These models help educators adapt course materials, assessments, and learning tasks to accommodate diverse student needs, thereby promoting a more inclusive learning environment. … While these advancements are beneficial for the entire student body, they are particularly effective for students with neurodiversity and cognitive or learning disabilities. For these students, AI-driven tools can significantly reduce barriers to learning, ensuring that education is accessible and tailored to meet a wide range of learner needs.”
Ethical Concerns and Risks
Despite these benefits, AI’s integration in higher education is not without its risks, particularly concerning privacy, data security, and the potential for AIdriven biases. With personal information often integral to AI functionality, data governance emerged as a significant focus in the Educause study, with nearly half of executives reporting they prioritize the development of AI-readiness for data handling. However, concerns over compliance with privacy regulations and ethical data use remain high.
Moreover, AI can inadvertently perpetuate and reinforce existing biases, because models learn from historical data that may carry or amplify societal prejudices. For example, if AI tools are used without human oversight to aid in recruitment or admissions, they may reproduce biases that disadvantage certain student groups, though the understanding of algorithmic discrimination is fragmented and requires further dedicated research.
Survey respondents pointed out that inappropriate uses of AI — such as relying solely on automated grading or using AI outputs without verifying accuracy.— could lead to unequal treatment of students, ultimately detracting from colleges’ missions to foster inclusive, supportive learning spaces.
“Like humans, AI can be prone to implicit biases due to flaws in data or algorithms,” wrote Maryam Roshanaei, PhD, assistant professor of information technology and cybersecurity analytics and operations at Pennsylvania State University Abington and author of a recent study addressing the implications of bias when using AI in academic settings. “These biases may cause discriminatory outcomes and decrease trust in AI. Bias in higher education admission may limit access to opportunities and further social inequalities, often due to implicit biases in data processing and decision-making. … To combat AI implicit biases, it is necessary to monitor and assess [the AI] performance and train them using unbiased data and algorithms.” 
Balancing Innovation and Integrity
Academic integrity is a pressing concern, as generative AI tools could facilitate plagiarism or cheating. The Educause study found that while AI is reshaping policies related to these concerns, only 23% of institutions have adequate guidelines in place.
Colleges are advised to approach policy development with care, creating clear standards around ethical AI use in coursework. For instance, some institutions mandate transparency by requiring students to disclose AI assistance in their assignments.
Rather than announcing blanket restrictions that simply disallow its use in coursework, institutions are encouraged to integrate AI literacy into curricula. Teaching students how to critically engage with the tools — and understand their limitations — fosters responsible digital citizenship. By doing so, colleges and universities can help students leverage AI for learning and skill development, rather than using it as a shortcut or replacement for academic effort.
A Path Forward
The Educause study highlights the need for institutions to approach AI with long-term, inclusive strategies that bridge departments and encourage collaboration with external partners. While most survey respondents feel that AI presents opportunities to enrich learning, many also noted the absence of institutional resources to support inclusive and accessible adoption. Investing in AI-specific roles and developing targeted training programs for faculty, staff, and students will be essential to realizing its potential equitably.
As AI continues to shape the future of higher education, its impact on access and belonging will depend on how institutions address these challenges. With thoughtful strategy, policy, and ongoing dialogue, colleges can harness AI to empower all students and build a more inclusive academic community
