Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general characteristics and distinguishing traits of algorithms, key domains in everyday life using algorithms (including the potential benefits and risks), and ethical considerations for the use and application of algorithms. Findings also suggested five behaviors that students could use to help them better cope with algorithmic systems and nine teaching strategies to help improve students’ algorithmic literacy. Suggestions also surfaced for alternative forms of assessment, potential placement in the curriculum, and how to distinguish between basic algorithmic awareness compared to algorithmic literacy. Recommendations for expanding on the current Association of College and Research Libraries’ Framework for Information Literacy for Higher Education (2016) to more explicitly include algorithmic literacy were presented.

Artificial Intelligence (AI) holds transformative potential for many fields including the finance sector. However, identifying suitable tasks for artificial intelligence implementation remains a challenge. This study proposes the artificial intelligence readiness task assessment tool, empowering finance professionals to assess task suitability for AI implementation from a bottom-up perspective. Artificial intelligence adoption often encounters barriers such as costs, compatibility, and skill gaps. The proposed tool addresses these challenges by allowing finance professionals to gauge artificial intelligence suitability for specific tasks without requiring extensive AI knowledge. The tool follows a design science research approach, ensuring it is user-friendly and effectively addresses real world challenges. The proposed tool is comprised of three sections: task framing, task assessment, and results interpretation. Unlike existing methodologies that focus on organization wide artificial intelligence readiness, the proposed tool centers on task specific readiness. This innovative approach provides practical guidance for finance professionals seeking to leverage artificial intelligence and helps organizations realize the potential of AI more effectively.

Digital Education: AI A.3:1 Einleitung 2 Das Projekt ELe-com 3 Das Zusammenspiel von EMIL und LENA 4 KI-unterstützte Empfehlungssysteme im Projekt ELe-com 5 Ausblick / Im Zuge der anhaltenden Weiterentwicklung und Verbreitung von KI und New Data Analytics wächst auch deren Bedeutung für den Einsatz im Bildungsbereich. Sie versprechen vielfältige Einsatzmöglichkeiten – gerade mit Blick auf Empfehlungssysteme, die zur Förderung und Verbesserung von Lehr-Lern-Prozessen beitragen können, indem sie eine stärkere Berücksichtigung individueller Lernpräferenzen erlauben. Neben der Personalisierung von Lernangeboten, erfüllen sie auch die zunehmenden, lebensweltlich geprägten Erwartungen von Nutzer:innen, die jenseits des klassischen Lernens vom wachsenden Einfluss neuer Formen der Informationsverbreitung und Kommunikation im Alltag geprägt sind.:1 Einleitung 2 Das Projekt ELe-com 3 Das Zusammenspiel von EMIL und LENA 4 KI-unterstützte Empfehlungssysteme im Projekt ELe-com 5 Ausblick