Nicholas Diana
Carnegie Mellon University, Pittsburgh, PA, USA
As the media landscape is increasingly populated by less than reputable sources of information, educators have turned to argument evaluation training as a potential solution. Unfortunately, the bias literature suggests that our ability to objectively evaluate an argument is, to a large extent, determined by the relationship between our own beliefs and the beliefs latent in the argument we are evaluating. If the argument supports our worldview, we are much more likely to overlook logical errors. Teachers recognize this need to adapt argument evaluation instruction to the specific beliefs of students. For instance, a teacher might intentionally assign a student an argument that the student disagrees with. Unfortunately, this kind of value-adaptive instruction is infrequent due to its unscalability. We propose a novel method for data-driven value-adaptive instruction in instructional technologies. This method can be used to combat bias in real-world contexts and support human reasoning during media consumption.
https://doi.org/10.1145/3313831.3376469
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)