When Artificial Intelligence Meets Educational Leaders' Data-Informed Decision Making: A Cautionary Tale

• In Event: The Complexity of Data-Based Decision Making: Evidence From the United States, Norway, New Zealand, and Belgium

Abstract

Purpose
Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision making. Drawing upon multidisciplinary literature that intersects AI, decision making, educational leadership, and policymaking, this position paper aims to examine promising applications and potential perils of AI in educational leaders’ data-informed decision making (DIDM).

Modes of inquiry
To seek answers to the questions, this article synthesizes the literature that intersecting AI, decision making, educational leadership, and policymaking from multiple disciplines such as computer science, educational leadership, administrative science, educational policy, judgment and decision making, and neuroscience.

Results
AI holds great potential in educational leaders’ DIDM. AI can improve the efficiency and accuracy of leaders’ DIDM. Endowed with ever-growing computational power and real-time data, leaders can identify teachers and students who need support in a timely manner. AI could be particularly helpful in online learning system where teaching and learning leave massive amounts of digital data.
On the other hand, misusing AI can have perilous effects on education stakeholders. First, many biases in current AI, if used without human judgment, could be amplified. These biases include, but not limited to, biases against students of color and special needs, as well as racial and gender biases in teacher/staff hiring and over group decision-making process. Second, current AI mostly has not taken into account the role of feelings and emotions in humans’ mental processes in decision making. Our feelings and emotions—from pleasure to pain, from passion to compassion, from empathy to disgust, and from guilt and regret—are part of the intuitions we rely on when making fast decisions that follow our gut feelings. Third, of more concern, the moral values (e.g., fairness, equity, honesty, and doing no harm) we uphold might clash with using AI to make data-informed decisions. When making decisions in which one person’s interest clashes with the group interest (e.g., in the case of a car accident, a driverless car chooses between killing one or five pedestrians), AI follows the utilitarian principle—maximizing the group interest and the greater good. This might be problematic when one special need student’s interest clashes with all students’ interest in a classroom. Another example would be the AI-assisted data-informed decisions of closing a school—due to the data on low student enrollment and low rating in a state’s accountability system—could be interpreted by the community as a cold-hearted decision.

Significance of the study
The article concludes with two recommendations for educational leaders and policymakers to leverage AI potential and minimize its negative consequences: (1) public scrutiny of AI, and (2) treating AI as a decision support, not a replacement.

Author

• Yinying Wang, Georgia State University