Retention and Transfer of Cognitive Bias Mitigation Interventions: A Systematic Literature Study

How Can Debiasing Research Aid Efforts to Reduce Discrimination?

PUBLIC ABSTRACT

Scientists studying intergroup biases are often concerned with lessening discrimination (unequal treatment of one social group versus another), but many interventions for reducing such biased behavior have weak or limited evidence. In this review article, we argue one productive avenue for reducing discrimination comes from adapting interventions in a separate field-judgment and decision-making-that has historically studied "debiasing": the ways people can lessen the unwanted influence of irrelevant information on decision-making. While debiasing research shares several commonalities with research on reducing intergroup discrimination, many debiasing interventions have relied on methods that differ from those deployed in the intergroup bias literature. We review several instances where debiasing principles have been successfully applied toward reducing intergroup biases in behavior and introduce other debiasing techniques that may be well-suited for future efforts in lessening discrimination.

Exploring the use of ChatGPT to analyze student course evaluation comments

BACKGROUND

Since the release of ChatGPT, numerous positive applications for this artificial intelligence (AI) tool in higher education have emerged. Faculty can reduce workload by implementing the use of AI. While course evaluations are a common tool used across higher education, the process of identifying useful information from multiple open-ended comments is often time consuming. The purpose of this study was to explore the use of ChatGPT in analyzing course evaluation comments, including the time required to generate themes and the level of agreement between instructor-identified and AI-identified themes.

METHODS

Course instructors independently analyzed open-ended student course evaluation comments. Five prompts were provided to guide the coding process. Instructors were asked to note the time required to complete the analysis, the general process they used, and how they felt during their analysis. Student comments were also analyzed through two independent Open-AI ChatGPT user accounts. Thematic analysis was used to analyze the themes generated by instructors and ChatGPT. Percent agreement between the instructor and ChatGPT themes were calculated for each prompt, along with an overall agreement statistic between the instructor and two ChatGPT themes.

RESULTS

There was high agreement between the instructor and ChatGPT results. The highest agreement was for course-related topics (range 0.71-0.82) and lowest agreement was for weaknesses of the course (range 0.53-0.81). For all prompts except themes related to student experience, the two ChatGPT accounts demonstrated higher agreement with one another than with the instructors. On average, instructors took 27.50 ± 15.00 min to analyze their data (range 20-50). The ChatGPT users took 10.50 ± 1.00 min (range 10-12) and 12.50 ± 2.89 min (range 10-15) to analyze the data. In relation to reviewing and analyzing their own open-ended course evaluations, instructors reported feeling anxiety prior to the process, satisfaction during the process, and frustration related to findings.

CONCLUSIONS

This study offers valuable insights into the potential of ChatGPT as a tool for analyzing open-ended student course evaluation comments in health professions education. However, it is crucial to ensure ChatGPT is used as a tool to assist with the analysis and to avoid relying solely on its outputs for conclusions.

Expanding Nature's storytelling: extended reality and debiasing strategies for an eco-agency

Communication in sustainability and environmental sciences is primed to be substantially changed with extended reality technology, as the emergent Metaverse gives momentum to building an urgent pro-environmental mindset. Our work focuses on immersive econarratives, supported by virtual and augmented realities, and their potential to favor an improved relationship with the environment. Considering social aggregation dynamics and cognitive bias, this article intends to (1) make the case for a new environmental narrative; (2) position extended reality as privileged settings to sustain this narrative; and (3) suggest that this storytelling should be informed by Nature's empirical evidence, i.e., ecosystem data. We see this as a chance to think this Metaverse with an embedded environmental consciousness, informed by behavior-change research.

Cognitive bias and how to improve sustainable decision making

The rapid advances of science and technology have provided a large part of the world with all conceivable needs and comfort. However, this welfare comes with serious threats to the planet and many of its inhabitants. An enormous amount of scientific evidence points at global warming, mass destruction of bio-diversity, scarce resources, health risks, and pollution all over the world. These facts are generally acknowledged nowadays, not only by scientists, but also by the majority of politicians and citizens. Nevertheless, this understanding has caused insufficient changes in our decision making and behavior to preserve our natural resources and to prevent upcoming (natural) disasters. In the present study, we try to explain how systematic tendencies or distortions in human judgment and decision-making, known as “cognitive biases,” contribute to this situation. A large body of literature shows how cognitive biases affect the outcome of our deliberations. In natural and primordial situations, they may lead to quick, practical, and satisfying decisions, but these decisions may be poor and risky in a broad range of modern, complex, and long-term challenges, like climate change or pandemic prevention. We first briefly present the social-psychological characteristics that are inherent to (or typical for) most sustainability issues. These are: experiential vagueness, long-term effects, complexity and uncertainty, threat of the status quo, threat of social status, personal vs. community interest, and group pressure. For each of these characteristics, we describe how this relates to cognitive biases, from a neuro-evolutionary point of view, and how these evolved biases may affect sustainable choices or behaviors of people. Finally, based on this knowledge, we describe influence techniques (interventions, nudges, incentives) to mitigate or capitalize on these biases in order to foster more sustainable choices and behaviors.