As artificial intelligence (AI) expands in higher education, institutions are exploring ways to harness its ability to expand competitiveness and innovation–but challenges remain, according to a new report.
The report, released by IDC in early March and commissioned by Microsoft, outlines the opportunity AI holds for the higher-ed community, as well as the challenges most institutions must overcome in order to take advantage of what AI offers.
The study of 509 institutions reveals that 99 percent say AI will be “instrumental” to their institution’s competitiveness over the next three years. Fifteen percent of those surveyed say AI is a game-changer for their institution, and 54 percent of institutions in the U.S. have started experimenting with AI. Thirty-eight percent have adopted AI as an essential component of their business strategy.
Key drivers for AI in higher education include increasing efficiencies and driving better student engagement, according to the report. Top use cases focus on improving experiences for students and prospective students, such as using AI technologies to make learning more accessible and inclusive.
In order to realize the potential of AI in higher education, institutions need to evolve and mature in five key areas:
1. Vision: Move from a place where AI is not considered part of the institutional strategy, with little to no AI investment, to a place where a proactive, innovative culture considers AI a game-changer.
2. People: Instead of little to no human-machine collaboration, human-machine collaboration should be a core part of processes and a high percentage of employees should have AI-related skill sets.
3. Process: An institution would move from being unaware of the business drivers and benefits of AI to strategically using AI to achieve key objectives.
4. Technology: Readiness in this area means that an institution has a centralized, dedicated team of developers, data scientists, and engineers across the entire AI model life cycle.
5. Data readiness: In lieu of a standalone data center, data is accessible to all business users through an enterprise data estate with well-managed quality control.
Key priorities for AI in higher education, for institutions of all sizes, include:
1. Modernized learning
2. Modernized classrooms
3. Modernized recruitment
4. Intelligent campus security
5. Optimized research administration
6. Intelligent facilities
7. Corporate relationship enhancement
8. Student success tracking
9. Research amplification
10. Collaborative library
Institutions’ top challenges for increasing AI in higher education include:
- Cost of the solution (57 percent)
- Lack of skills, resources, and continuous learning programs (47 percent)
- Data strategy and data readiness are not seen as strategic priorities (37 percent)
Across institutions of all sizes, culture seems to be the biggest strength in terms of readiness for embracing AI in higher education. Most institutions believe their education leadership encourages staff proactivity and initiative, expecting bottom-up innovations rather than execution of top-down decisions. Education staff are encouraged to partner within their own unit and across the institutions both vertically and horizontally, according to those surveyed.
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