AI for Social Good - Australian Attitudes Toward AI and Society Report.pdf

Selwyn, Neil; Cordoba, Beatriz Gallo; Andrejevic, Mark; Campbell, Liz

doi:10.26180/13159781.v1

Cited by 6 publications

(10 citation statements)

References 0 publications

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“…In general, participants performed similar in both knowledge tests with about 60% to 65% correct answers. This is in line with previous research showing that the public holds relatively little knowledge about AI and AI devices (Selwyn et al, 2020;Merenkov et al, 2021).…”

Section: Influence Of Cognitive Factors On Risk and Opportunity Perce...supporting

confidence: 92%

“…However, as previously mentioned, the ever-growing use of AI applications has sown unease amog US citizens (Oliver, 2022) Zhang & Dafoe (2019) also found that 82% of participants expressed that AI should be carefully managed, despite the majority of participants supporting AI use. A similar ambiguous trend can be seen for the Australian population where AI in healthcare and medicine is seen a positive while AI use in the workplace is met with doubts (Selwyn et al, 2020). These findings suggest that public perception of AI seems to be rather ambiguous: posing risks and opportunities at the same time.…”

Section: Artificial Intelligence Applicationsmentioning

confidence: 63%

“…Research on knowledge about AI consistently showed that the public holds extraordinarily little knowledge about AI and AI devices (Selwyn et al, 2020;Merenkov et al, 2020). The general public seems to still be confused about what AI is and where AI is being used (Edelman, 2019).…”

Section: Knowledge About Ai Applicationsmentioning

confidence: 99%

“…Furthermore, the public also tends to underestimate the prevalence of AI in everyday technology (Zhang & Dafoe, 2019). Currently, the general population's level of knowledge on the topic of AI already is mainly assessed in form of surveys (Rathi & Rathi, 2019;Selwyn et al, 2020;Selwyn & Cordoba, 2021;Bao et al, 2022), with only few questions about AI knowledge mixed with attitudes towards AI, trust in, and familiarity with AI. A validated AI knowledge test is currently missing in literature.…”

Section: Knowledge About Ai Applicationsmentioning

confidence: 99%

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An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

Said¹,

Potinteu²,

Brich³

et al. 2022

Preprint

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Artificial intelligence (AI) applications are increasingly used in everyday life. Whereas some of them are widely accepted (e.g., automatically compiled playlists), others are highly controversial (e.g., use of AI in the classroom). While public discourse is dominated by perceptions of the risks associated with AI, we take a fundamentally different approach of measuring the perceived risks and opportunities of AI applications considering people's knowledge and confidence in their own knowledge. To this end, we assessed in two studies (N = 394 and N = 437) how knowledge about AI as well confidence in AI knowledge is related to participants risk-opportunity perception of AI scenarios from three domains: media, medicine, and autonomous driving. Results showed that both AI knowledge and confidence in AI knowledge are important predictors regarding people’s risk-opportunity perception beyond people's attitudes towards AI. More specifically, people with more knowledge about AI exhibited a so-called risk blindness in that they were underestimating the risks. On the other hand, higher confidence in ones’ AI knowledge impacted participants opportunity perception. Knowledge and confidence thus open a new dimension of understanding people’s perception of risks and opportunities in AI.

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Section: Influence Of Cognitive Factors On Risk and Opportunity Perce...supporting

confidence: 92%

Section: Artificial Intelligence Applicationsmentioning

confidence: 63%

Section: Knowledge About Ai Applicationsmentioning

confidence: 99%

Section: Knowledge About Ai Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

Said¹,

Potinteu²,

Brich³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This supportive attitude resonates with findings in the UK, where adults showed favourable views about using artificial intelligence to tailor teaching and learning to individuals (Ipsos/ MORI, 2017). A recent study also found that around 71% of Australian adults support the use of artificial intelligence to address educational challenges (Selwyn et al, 2020). These findings encourage academicians, practitioners, and researchers to further investigate the potentials of practically adopting educational technologies to support learning and teaching practices.…”

Section: Ethical Considerationsmentioning

confidence: 82%

Mapping from proximity traces to socio‐spatial behaviours and student progression at the school

Yan

Martínez-Maldonado

Cordoba

et al. 2022

Brit J Educational Tech

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Identifying students facing difficulties and providing them with timely support is one of the educator's key responsibilities. Yet, this task is becoming increasingly challenging as the complexity of physical learning spaces grows, along with the emergence of novel educational technologies and classroom designs. There has been substantial research and development work focused on identifying student social behaviours in digital platforms (eg, the learning management system) as predictors of academic progression. However, little work has investigated such relationships in physical learning spaces. This study explores the potential of using wearable trackers for the early detection of low‐progress students based on their social and spatial (socio‐spatial) behaviours at the school. Positioning data from 98 primary school students and six teachers were automatically captured over a period of eight weeks. Fourteen socio‐spatial behavioural features were extracted and processed using a set of machine learning classifiers to model students’ learning progression. Results illustrate the potential of prospectively identifying low‐progress students from these features and the importance of adapting classroom learning analytics to differences in pedagogical designs. What is already known about this topic Learning analytics research on predicting students’ academic progression is emerging in both digital and physical learning spaces. Students’ social behaviours in learning activities is a key factor in predicting their academic progression. Emerging sensing technologies can provide opportunities to study students’ real‐time social behaviours in physical learning spaces. What this paper adds Fourteen progression‐related socio‐spatial behavioural features are extracted from students’ physical (x‐y) positioning traces. Predictive learning analytics that achieved 81% accuracy in prospectively identifying low‐progress students from their real‐time socio‐spatial behaviours. Empirical evidence to support the need for classroom learning analytics to have instructional sensitivity (ie, be calibrated according to the learning design). Implications for practice and/or policy Sensing technologies and machine learning algorithms can be used to capture and generate valuable insights about higher‐order learning constructs (eg, performance and collaboration) from students' physical positioning traces in classrooms. Researchers and practitioners should be cautious with generalised classification algorithms and predictive learning analytics that do not account for the pedagogical differences between different subjects or learning designs. Researchers and practitioners should consider the potentially unforeseen ethical issues that can emerge in using sensing technologies and predictive learning analytics in authentic, physical classroom settings.

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An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception

Said

Potinteu

Brich

et al. 2023

Computers in Human Behavior

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AI for Social Good - Australian Attitudes Toward AI and Society Report.pdf

Cited by 6 publications

References 0 publications

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

An Artificial Intelligence Perspective: How Knowledge and Confidence Shape Risk and Opportunity Perception

Mapping from proximity traces to socio‐spatial behaviours and student progression at the school

An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception

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