Schlagwort: seven

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Blogpost on „New Report: Seven Myths of AI Use – A Critical Perspective on Generative AI“

Thanks to Stefanie, who published a blogpost about our „Seven Myths of AI Use – A Critical Perspective on Generative AI„:

These days, people can hardly use the Internet without running into generative AI—yet many everyday beliefs about “how AI works” are inaccurate in ways that matter especially for education, argues the position paper “Seven Myths of AI Use”. It was published by a team of six Austrian researchers: Sandra Schön, Benedikt Brünner, and Martin Ebner from Graz University of Technology (TU Graz), Sarah Diesenreither and Georg Krammer from Johannes Kepler University Linz, and Barbara Hanfstingl from the University of Klagenfurt. At a time when we start to contemplate futures of “a country of geniuses in a datacenter” (Dario Amodei), this report summarizes some of the most prominent concerns towards generative AI.

Find here the full blogpost.

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Seven Myths of AI use

Here is the English version of our short report we have already published in German here.

In 2026, it is unavoidable to use the internet without encountering Artificial Intelligence (in short, AI). Search engines do not just search, but offer next to links answers generated by AI, chatbotshelp you with your bookings on websites, pupils in school generate themselves exemplary exams based on the study materials offered by their teachers, and so forth. However, neither outputs based on AI nor our usage thereof nor how we interpret it is accurate and without inherent problems. Partially, this is due to users’ misconceptions on how AI tools function. From where we are standing – and this is prior to empirical evidence – we argue that the following seven statements about AI warrant heightened attention, especially viewed with their implications for education, schools and universities:

1.AI tools are neutral, objective and unbiased
2.AI tools function logically
3.AI tools think and learn like humans
4.AI tools are empathetic
5.AI tools are ecologically and socially unproblematic
6.AI tools act in accordance with the law
7.AI tools render knowledge and competence acquisition obsolete

In this article we address these so-called “Myths of AI use” and highlight that the underlying notions are not true and why so. We do so aiming to raise awareness, and to stimulate and support prospective research on AI myths

[full version @ OSF]
[full version @ ResearchGate]

Reference: Schön, S., Brünner, B., Ebner, M., Diesenreither, S., Hanfstingl, B., & Krammer, G. (2026, February 13). Seven Myths of AI use. Preprint. DOI: 10.35542/osf.io/6wnyd_v1

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[publication] Seven features of smart learning analytics – lessons learned from four years of research with learning analytics

Together with Behnam Taraghi, Anna Saranti and Sandra Schön we discussed and broad together what makes learning analytics smart – from our perspectives and experiences with some years of work (and several publications). Here your will find the whole publication or simply summarized as figure:

Folie1

Abstract:

Learning Analytics (LA) is an emerging field; the analysis of a large amount of data helps us to gain deeper insights into the learning process. This contribution points out that pure analysis of data is not enough. Building on our own experiences from the field, seven features of smart learning analytics are described. From our point of view these features are aspects that should be considered while deploying LA.

Reference: Martin Ebner, Behnam Taraghi, Anna Saranti, Sandra Schön (2015). Seven features of smart learning analytics – lessons learned from four years of research with learning analytics. In: eLearning Papers, Issue 40, January 2015, pp. 51.55, URL: https://www.openeducationeuropa.eu/en/article/Assessment-certification-and-quality-assurance-in-open-learning_From-field_40_3?paper=164347