Smart Machines & Human Expertise: Challenges for Higher Education — from er.educause.edu by Diana Oblinger
Excerpts:
What does this mean for higher education? One answer is that AI, robotics, and analytics become disciplines in themselves. They are emerging as majors, minors, areas of emphasis, certificate programs, and courses in many colleges and universities. But smart machines will catalyze even bigger changes in higher education. Consider the implications in three areas: data; the new division of labor; and ethics.
Colleges and universities are challenged to move beyond the use of technology to deliver education. Higher education leaders must consider how AI, big data, analytics, robotics, and wide-scale collaboration might change the substance of education.
Higher education leaders should ask questions such as the following:
- What place does data have in our courses?
- Do students have the appropriate mix of mathematics, statistics, and coding to understand how data is manipulated and how algorithms work?
- Should students be required to become “data literate” (i.e., able to effectively use and critically evaluate data and its sources)?
…
Higher education leaders should ask questions such as the following:
- How might problem-solving and discovery change with AI?
- How do we optimize the division of labor and best allocate tasks between humans and machines?
- What role do collaborative platforms and collective intelligence have in how we develop and deploy expertise?
…
Higher education leaders should ask questions such as the following:
- Even though something is possible, does that mean it is morally responsible?
- How do we achieve a balance between technological possibilities and policies that enable—or stifle—their use?
- An algorithm may represent a “trade secret,” but it might also reinforce dangerous assumptions or result in unconscious bias. What kind of transparency should we strive for in the use of algorithms?