AI in Higher Education: Enhancing, Not Replacing, Human Decision Making

Collaboration between AI and human expertise will help higher education remain innovative.

GUEST COLUMN | by Andy Hannah

Artificial intelligence (AI) is rapidly becoming an integral part of decision-making processes across various sectors, including higher education. While some fear that AI might replace human judgment, the reality is that AI serves as a powerful tool to enhance human expertise. By understanding how AI can complement human decision making—particularly in admissions—institutions can harness the technology to support and amplify human insights, leading to better outcomes for students and educators alike.

‘By understanding how AI can complement human decision making—particularly in admissions—institutions can harness the technology to support and amplify human insights, leading to better outcomes for students and educators alike.’

In graduate admissions, for example, AI tools can assist in evaluating candidates by analyzing large datasets to identify patterns and insights that may not be immediately apparent to human evaluators. This is particularly useful in holistic admissions, when schools seek to align their selection criteria with their institutional mission and values. AI can map out desired qualities and experiences in candidates that are challenging to quantify, such as grit and empathy. By optimizing the composition of diverse student cohorts, AI makes sure that while certain criteria are met, the uniqueness of each applicant is preserved, preventing the formation of a homogeneous student body.

The Integration of AI in Higher Education Admissions

Successfully integrating AI into the admissions process requires a strategic focus on three key pillars: skill set, technology, and data. Institutions must cultivate a skilled workforce and prioritize staff training to fully harness AI’s potential. The complexity of AI demands professionals who can navigate its powerful tools and platforms effectively. In customer relationship management (CRM) systems, for instance, automating communication tasks offers significant benefits, but these can only be realized if employees understand and take ownership of the software.

Technology plays a pivotal role in both the challenges and opportunities associated with AI integration. Traditional statistical methods, such as correlations, often fall short in capturing the complexities of individual candidates. Institutions need to move beyond these linear models and embrace more sophisticated, nonlinear approaches that provide a richer, more nuanced understanding of applicants.

Finally, the accessibility and quality of data are critical. Despite the increasing availability of data, many institutions struggle with effective data collection and management, which is essential for AI to deliver accurate and meaningful insights.

Balancing AI and Human Expertise

Incorporating AI into data-driven decision making in education requires a careful balance between technology and human judgment. While AI offers powerful tools for analyzing data and identifying patterns, human expertise remains essential for interpreting these insights and making contextually appropriate decisions. Challenges arise when institutions rely too much on AI and risk losing the nuanced understanding that only human experts bring to complex situations. The potential for bias in AI-driven decisions is another major concern, particularly when algorithms are based on historical data that may reflect existing inequities. Institutions must confirm that their AI systems are designed with fairness and transparency from the outset.

To ensure AI enhances rather than replaces human judgment while remaining ethical, institutions should involve human oversight in the final decision-making stages, thereby preserving the integrity and inclusivity of their decisions. Institutions that adhere to the following best practices can use AI to enhance human judgment and contribute to better outcomes in higher education:

• Transparency: Clearly communicate how AI tools are employed in decision-making processes to build trust among stakeholders. Transparency helps all parties understand the role of AI in higher education in shaping outcomes.
• Continuous Staff Training: Invest in ongoing training so that users of AI systems fully understand their capabilities and limitations, enabling them to make more informed decisions.
• Rigorous Testing and Validation: Implement thorough testing and validation procedures to maintain the accuracy, reliability, and fairness of AI tools, making sure they perform as expected.
• Bias Mitigation: Regularly audit AI algorithms and use diverse and representative datasets to identify and mitigate biases, fostering equitable decision making.
• Ethical Use: Ethical considerations encompass a range of issues, including data privacy, consent, and the potential impact of AI on individuals and communities. Institutions must uphold ethical principles throughout their AI practices.

As higher education continues to evolve, the integration of AI into decision-making processes presents both significant opportunities and challenges. By viewing AI as a way to complement rather than replace human expertise, institutions can enhance their decision-making capabilities while maintaining the critical human touch that defines education. Through careful planning and adherence to best practices, AI can increase efficiency, improve outcomes, and promote fairness. Ultimately, the successful integration of AI in higher education will depend on the ongoing collaboration between technology and human insight, keeping the future of education both innovative and inclusive.

—

Andy Hannah is the president of Liaison’s AI and Data Science Solution, Othot. In his role, he promotes the use of artificial intelligence and prescriptive analytics, enabling colleges and universities to understand their students better and make informed decisions throughout the entire student-to-alumni lifecycle. Andy is also an adjunct professor of analytics at the University of Pittsburgh and chairperson of the University of Pittsburgh’s Responsible Data Science Advisory Board. Connect with Andy on LinkedIn.

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to share on LinkedIn (Opens in new window)

Like this:

Like Loading…

Related