Abstract
Social Media Artificial Intelligence algorithms provide users with engaging and personalized content. Yet, the personalization of algorithms may have a negative impact on users who lack AI literacy. The limited understanding of SM algorithms among the population suggest that adolescents are more likely to place blind trust in the information they consume, exposing them to negative consequences (misinformation, filter bubbles and echo chambers). We therefore propose an intervention with a narrative scripts approach to raise awareness of AI algorithms in SM. To foster an authentic learning experience and question adolescents’ trust in AI, we deploy a low-accuracy AI image classifier. A quasi-experimental study was conducted among 144 high-school students in Barcelona, Spain. The results show that the narrative scripts intervention improved students’ awareness of SM algorithms and shaped more critical attitudes towards them. A comparison of students’ choices between human predictions and those produced by a low-accuracy AI classifier shows a lack of AI overdependence. Information about predictions’ source did not affect students’ trust or learning about AI. These findings contribute towards SM algorithms education and share insight into the effect of deploying low-accuracy detectors in learning technology interventions.
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Acknowledgement
This work has been partially funded by the Volkswagen Foundation (COURAGE project, no. 95567). TIDE-UPF also acknowledges the support by AEI/10.13039/ 501100011033 (PID2020-112584RB-C33, MDM-2015-0502), ICREA under the ICREA Academia programme (D. Hernández-Leo, Serra Hunter) and the Department of Research and Universities of the Government of Catalonia (SGR 00930).
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Theophilou, E., Lomonaco, F., Donabauer, G., Ognibene, D., Sánchez-Reina, R.J., Hernàndez-Leo, D. (2023). AI and Narrative Scripts to Educate Adolescents About Social Media Algorithms: Insights About AI Overdependence, Trust and Awareness. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_28
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