Leveraging AI for institutional improvement, continuous learning

Key points:

• Success with AI requires transparency and cross-departmental collaboration
• AI skills could eclipse college degrees
• Despite privacy concerns, higher ed’s AI adoption surges
• For more news on AI in higher ed, visit eCN’s AI in Education hub

With the advent of AI tools comes the for higher-ed leaders innovate at all levels, automating administrative and repetitive tasks and using AI to transform outdated processes and open up cross-departmental collaboration.

During an EDUCAUSE 2024 online session, Jeremy Gatens, IT Director at Core HR at the University of Pennsylvania, explored AI’s potential in aligning university operations with future-ready outcomes.

Through targeted pilot projects, university leaders can explore AI-driven strategies to empower cross-department collaboration. By fostering collaboration and innovation, higher ed can bridge the AI readiness gap and prepare institutions and students for the demands of the future.

“AI is not just a technological advancement–it’s a catalyst for transformation across industries,” Gatens noted. “In education, AI can personalize learning experiences, automate administrative tasks, and open new avenues for research and innovation.” But despite its potential, many institutions feel unprepared–HED institutions typically rate themselves at a 4 or 5 out of 10 when it comes to AI readiness, Gatens added, highlighting the significant gap between where higher ed is today and where it needs to be to meet the needs and expectations of staff and students.

Leveraging AI to enhance learning and prepare the workforce for the future

“Our task is to demystify AI and harness it effectively in our organizations,” Gatens said. “It’s important to get AI in the hands of the people. To truly benefit from AI, we need to get it in the hands of executives and IT professionals who can drive its adoption. This means we need improved AI integration strategies and, importantly, cross-departmental collaboration.”

Successful AI implementation hinges on 3 key elements: involving subject matter experts at every stage, using an iterative pilot approach to validate results, and focusing on user experience. There also is a need for improved AI integration strategies.

The rapid advancement of AI offers unprecedented opportunities in education and career development. The technology has and will continue to have a transformative impact on education–AI can personalize learning, automate grading, and offer insight on student performance. New AI-related jobs are emerging, requiring a workforce skilled in AI. By embracing AI, higher ed can enhance educational outcomes and better prepare students for the evolving job market.

Pilots are pivotal to AI programs

Launching trial programs can drive innovation within higher-ed organizations, Gatens observed. Trials or pilots let higher-ed leaders test new ideas on a small scale before broad implementation, allowing for exploration of AI’s potential with minimal risk as pilot leaders gain valuable insights and lessons.

Fostering innovation through collaboration is key to these trial programs. Bringing together diverse teams from different departments offers varied skills, experiences, and perspectives–leading to more effective and innovative solutions. This collaborative environment encourages creativity and out-of-the-box thinking.

“Launching trial programs contributes to cultivating an innovative culture and signals to employees that experimentation and continual improvement are valued,” Gatens said.

Examining longstanding institutions processes is critical when moving forward with AI. “To truly innovate, we must be willing to examine and challenge longstanding work processes. This involves critically assessing workflows and identifying areas where AI can introduce efficiencies and improvements. Questioning the status quo opens the door to transformative changes,” Gatens said.

At the University of Pennsylvania, an AI-integrated resume reviewer opened doors for a new hiring system. Through the Resume Café pilot program, IT teams partnered with talent acquisition experts for a pilot that was used at a recent job fair. Applicants uploaded their resumes to the AI resume reviewer. The AI reviewed resumes and highlighted strengths and areas of improvement in each candidate’s resume. Out of 47 applicants in the pilot, seven entered the job pipeline.

“This pilot showcases how AI can enhance our recruitment process by providing immediate, valuable feedback to applicants–and streamlining candidate selection for us,” Gatens said.

Empowering universities to empower faculty and students

Using AI will empower the new workforce, will help position institutions to align with new workforce needs. Embracing AI collaboratively not only improves university operations, but improves the ability to attract retain talent in an increasingly competitive market.

Building cross-department product units to enhance AI initiatives: Forming cross-department teams brings together diverse expertise and perspectives. This ensures that AI solutions are well-rounded and address the actual needs of various departments. It fosters innovation and accelerates problem solving by breaking down silos in the organization.

Measurable and validated results: Working collaboratively allows all those involved to establish clear metrics and key performance indicators from the outset. Institutions and teams can measure and validate the results of AI projects more effectively, ensuring they deliver tangible value. This data-driven approach demonstrates ROI and helps in making informed decisions for future initiatives.

Value-based collaboration: Data-driven organizations that emphasize value-based collaboration significantly outperform their peers. By aligning AI efforts with organizational values and strategic goals, leaders can enhance efficiency, adaptability, and competitiveness in the market.

Challenges in AI adoption

Despite AI’s potential, it’s essential to remain mindful of its challenges.

Data quality and availability: Poor data quality can hinder AI effectiveness. We need robust data management practices, cleaning, validation, and enrichment to ensure AI systems have access to reliable and comprehensive data.

Skill gaps: Investing in training programs and partnering with education intuitions can bridge this gap, equipping workforce with necessary skills.

High costs: AI projects can be expensive. Starting with pilots can demonstrate ROI on a smaller scale before larger investments. Exploring cloud-based AI solutions can reduce upfront costs.

Ethical concerns: Ethical issues like bias and lack of transparency can impact trust in AI systems. Developing a clear ethical framework and implementing practices for transparency and accountability are essential.

Resistance to change: Employees may resist AI due to fear of job loss or unfamiliarity. A change management strategy with clear communication and training can help.

Security risks: AI systems can introduce new security vulnerabilities. Implementing robust cybersecurity measures such as regular audits and threat detection is critical.

Measuring ROI: Quantifying AI’s benefits can be challenging. Establishing clear metrics and regularly reviewing them ensures we accurately capture value of AI initiatives. Cross-departmental collaboration can enhance AI adoption strategies, drive innovation, and achieve success.

Maintaining transparency and championing continuous learning

“Implementing AI is a continuous journey that requires transparency and adaptability,” Gatens said.

Establish an AI governance framework:

• Capture opportunities correctly–ensure AI initiatives align with organizational goals
• Define roles and responsibilities–clarify who is responsible for what
• Track and measure generative AI success–use metrics to assess AI solutions performance
• Address ethical concerns throughout the process–be mindful of bias and privacy
• Maintain strong security practices–protect data integrity and confidentiality

For higher education leaders

• Evaluate organizational roles–assess the effectiveness of roles within your organization and identify how AI can augment existing roles or create new opportunities
• Review existing talent–consider your current talent pipeline and assess whether your institution is cultivating the skills needed for an AI-driven future
• Plan for future needs–develop a game plan and create a strategic road map for AI integration

“By fostering a culture of transparency and continuous learning, we can adapt to changes and optimize our AI strategies over time,” Gatens added.