Figure 2
The graph is titled “Trends in the Application of Fairness-Aware Algorithms in Various Educational Settings”. The horizontal axis is labeled “Year” and ranges from left to right from 2015 to 2022 in increments of 1 year. The vertical axis is labeled “Adoption or Effectiveness Score” and ranges from 50 to 85 in increments of 5 units. A legend in the top left indicates that the graph plots four lines. The line labeled “Public Schools” starts from (2015, 60), rises upward diagonally with slight fluctuations, and terminates at (2022, 80). The line labeled “Private Schools” starts from (2015, 55), rises upward diagonally with slight fluctuations, and terminates at (2022, 78.28). The line labeled “Universities” starts from (2015, 70), rises upward diagonally with slight fluctuation, and terminates at (2022, 85). The line labeled “Online Education Platforms” starts from (2015, 50), rises upward, and terminates at (2022, 75). Note: All numerical data values are approximated.Visualisation of the adoption and implementation rates of fairness-aware algorithms and bias mitigation strategies across educational AI applications, highlighting significant disparities in practice. Source: created by the author based on systematic literature review findings (Baker and Hawn, 2022; Mehrabi et al., 2021; Kizilcec and Lee, 2022)