Gender equality in education has been a longstanding goal, but humans’ gender-biased beliefs make it an elusive goal. Zhengyang Bao, Difang Huang and Chen Lin did an experiment in which students were divided in two groups taught by either human or AI teachers. They found that AI teachers successfully reduced gender performance gaps.

Education plays a central role in improving individuals’ life achievements and fostering socioeconomic development more broadly. It is crucial for educators, researchers and policymakers to enhance educational outcomes and create an inclusive and equitable learning environment.

However, teachers and mentors may harbour gender-biased beliefs, which can be slow to change. Evidence suggests that discriminatory practices and stereotypes identified in classrooms may keep students from reaching their full potential, leading to gender performance gaps later on. Gender discrimination can emerge as early as in primary schools and promoting gender equality in education has been a critical social goal for an a long time.

The advancement of AI technology offers a potential solution to this issue. AI has already been extensively used for educational purposes, and gender-related variables can be removed from AI input to enhance gender neutrality. Nonetheless, there is limited evidence on the effectiveness of AI training, particularly its potential benefits in reducing gender performance gaps.

A natural experiment with AI teachers

In our study, we analyse data from a natural experiment where AI replaced a random subset of human teachers at a training agency for Go, an abstract strategy board game for two players. This field setting is ideal for a comprehensive examination of AI teaching for several reasons.

First, students must attend two training sessions per week, each consisting of a tournament and a match review with their teachers. This straightforward training structure allows us to study how a teacher’s guidance during reviews can enhance students’ Go skills in subsequent games. The frequent gameplay provides ample observations to assess students’ skill development and allows us to draw statistical inferences.

Second, Go is a male-dominated game with a pre-existing gender performance gap, enabling us to address our research question.

Third, due to China’s COVID prevention regulations, both tournaments and reviews were conducted online during the analysis period. The Go-playing software recorded all moves, facilitating the quantification of student performance. We also recorded review sessions led by both human and AI teachers to investigate the relationship between teachers’ facial, vocal and verbal characteristics, and student learning outcomes.

The AI teacher is depicted as a cartoon character on the screen, providing facial, vocal, and verbal information for analysis, similar to human teachers. Furthermore, student-teacher interactions occur online for both AI and human teachers, ensuring a fair comparison between the two teaching methods. Additionally, the AI tool does not collect any student information (such as gender) beyond the moves made in each tournament, meeting crucial study requirements.

AI teaching outcomes

We conducted a comprehensive analysis of tournament data for two groups: students taught solely by human teachers and students who received training from the AI teacher after the intervention. Before the introduction of AI training, both groups exhibited parallel performance trends in Go. However, after the intervention, the group trained by AI demonstrated significantly faster improvement in Go skills compared to the other group. This suggests that the AI teacher is more effective than human teachers in enhancing students’ learning outcomes across the entire student population.

Next, we examined the impact of AI teaching on boys and girls separately. We observed a persistent gender performance gap before the experimental intervention, with boys performing significantly better than girls. After introducing AI, we found that both genders in the group taught by AI progressed faster than their counterparts in the other group, with girls advancing more rapidly than boys under AI training. Among the treated group, boys and girls achieved similar performance after five months of AI training.

How does AI teaching affect learning?

In the next step, we asked teaching-related questions to students who were taught by both AI and humans, to understand the potential channels driving the effects. Survey results show that AI’s ability to analyse games, provide relevant statistics and offer interactive features helped students learn faster. However, we didn’t find any significant gender differences in responses to these questions. We also discovered that students tend to perceive the AI teacher’s appearance as more attractive than that of human teachers, with girls having a stronger preference for AI’s appearance than boys. Students also claimed that the attractiveness of teachers positively correlates with learning outcomes.

We further analysed video recordings of all revision classes to study the reasons behind the initial gender performance gap and how AI training can reduce it. The data indicates that human teachers displayed more positive and fewer negative emotions towards boys and students with more advanced Go skills. The data reveals significant correlations between teachers’ emotional status in the revision class and students’ performance in subsequent games. However, the AI teacher’s emotions had less variation than human teachers and were not gender dependent.

Consistent with the video evidence, the survey results indicate that girls can discern gender-biased emotions exhibited by human teachers and this may influence their learning outcomes. They did not perceive gender bias in AI teachers, which indicates that teachers’ emotional status may explain the evolution of the gender performance gap.

Takeaways

AI teachers’ inherent advantages in analysing game data and providing relevant information and friendly interactive features help students learn faster, and their gender-neutral emotions can help reduce the gender performance gap.

We believe the results may apply to the training of managers and policymakers, who rely on intuition to solve complex problems under uncertainty and time constraints, a decision-making style mirrored by Go.

AI excels in detecting discrimination and subtle emotional changes, offering a means to mitigate bias and gather valuable information when participants’ emotions reveal their thoughts during business negotiations, court hearings, and political discussions. Our findings have implications for AI’s instructional role in enhancing decision-making for managers and policymakers.

• This blog post is based on Can Artificial Intelligence Improve Gender Equality? Evidence from a Natural Experiment, Management Science.
• The post represents the views of the author, not the position of LSE Business Review or the London School of Economics and Political Science.
• Featured image provided by Shutterstock.
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