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  1. Article ; Online: To Advance AI Use in Education, Focus on Understanding Educators.

    Kizilcec, René F

    International journal of artificial intelligence in education

    2023  , Page(s) 1–8

    Abstract: A better understanding of educators' perspectives of emerging education technology, specifically tools that incorporate AI, is essential to unlock the full potential benefits of these innovations. While prior research has primarily emphasized ... ...

    Abstract A better understanding of educators' perspectives of emerging education technology, specifically tools that incorporate AI, is essential to unlock the full potential benefits of these innovations. While prior research has primarily emphasized technological advancements, it has overlooked the profound influence of social, psychological, and cultural factors in shaping educators' perceptions, trust, and adoption of educational technology. As increasingly powerful AI tools emerge, their design must be rooted in a deep understanding of educators' needs and perspectives. It is only with the acceptance and trust of educators that these innovative solutions can elevate learning outcomes, academic achievements, and educational equity.
    Language English
    Publishing date 2023-06-09
    Publishing country England
    Document type Editorial
    ZDB-ID 2071388-5
    ISSN 1560-4306 ; 1560-4292
    ISSN (online) 1560-4306
    ISSN 1560-4292
    DOI 10.1007/s40593-023-00351-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: A new model of trust based on neural information processing

    Allen, Scott E. / Kizilcec, René F. / Redish, A. David

    2024  

    Abstract: More than 30 years of research has firmly established the vital role of trust in human organizations and relationships, but the underlying mechanisms by which people build, lose, and rebuild trust remains incompletely understood. We propose a mechanistic ...

    Abstract More than 30 years of research has firmly established the vital role of trust in human organizations and relationships, but the underlying mechanisms by which people build, lose, and rebuild trust remains incompletely understood. We propose a mechanistic model of trust that is grounded in the modern neuroscience of decision making. Since trust requires anticipating the future actions of others, any mechanistic model must be built upon up-to-date theories on how the brain learns, represents, and processes information about the future within its decision-making systems. Contemporary neuroscience has revealed that decision making arises from multiple parallel systems that perform distinct, complementary information processing. Each system represents information in different forms, and therefore learns via different mechanisms. When an act of trust is reciprocated or violated, this provides new information that can be used to anticipate future actions. The taxonomy of neural information representations that is the basis for the system boundaries between neural decision-making systems provides a taxonomy for categorizing different forms of trust and generating mechanistic predictions about how these forms of trust are learned and manifested in human behavior. Three key predictions arising from our model are (1) strategic risk-taking can reveal how to best proceed in a relationship, (2) human organizations and environments can be intentionally designed to encourage trust among their members, and (3) violations of trust need not always degrade trust, but can also provide opportunities to build trust.
    Keywords Economics - General Economics ; Computer Science - Human-Computer Interaction ; Quantitative Biology - Neurons and Cognition
    Subject code 650
    Publishing date 2024-01-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Identifying course characteristics associated with sociodemographic variation in enrollments across 159 online courses from 20 institutions.

    Kizilcec, René F / Kambhampaty, Anna

    PloS one

    2020  Volume 15, Issue 10, Page(s) e0239766

    Abstract: Millions of people worldwide use online learning for post-secondary education and professional development, but participation from historically underrepresented groups remains low. Their choices to enroll in online courses can be influenced by course ... ...

    Abstract Millions of people worldwide use online learning for post-secondary education and professional development, but participation from historically underrepresented groups remains low. Their choices to enroll in online courses can be influenced by course features that signal anticipated success and belonging, which motivates research to identify features associated with sociodemographic variation in enrollments. This pre-registered field study of 1.4 million enrollments in 159 online courses across 20 institutions identifies features that predict enrollment patterns in terms of age, gender, educational attainment, and socioeconomic status. Among forty visual and verbal features, course discipline, stated requirements, and presence of gender cues emerge as significant predictors of enrollment, while instructor skin color, linguistic style of course descriptions, prestige markers, and references to diversity do not predict who enrolls. This suggests strategic changes to how courses are presented to improve diversity and inclusion in online education.
    MeSH term(s) Adult ; Computer-Assisted Instruction/statistics & numerical data ; Curriculum/statistics & numerical data ; Education, Distance/statistics & numerical data ; Female ; Humans ; Male ; Social Class
    Language English
    Publishing date 2020-10-14
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0239766
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  4. Book ; Online: Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions

    Lee, Hansol / Kizilcec, René F. / Joachims, Thorsten

    2023  

    Abstract: A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for ... ...

    Abstract A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. However, this approach may be subject to bias in test scores and selection bias in test-taking with recent trends toward test-optional admission. We explore a machine learning-based approach to replace the role of standardized tests in subset generation while taking into account a wide range of factors extracted from student applications to support a more holistic review. We evaluate the approach on data from an undergraduate admission office at a selective US institution (13,248 applications). We find that a prediction model trained on past admission data outperforms an SAT-based heuristic and matches the demographic composition of the last admitted class. We discuss the risks and opportunities for how such a learned model could be leveraged to support human decision-making in college admissions.

    Comment: In Proceedings of the ACM Conference on Learning at Scale (L@S) 2023
    Keywords Computer Science - Computers and Society
    Publishing date 2023-02-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Pandemic response policies' democratizing effects on online learning.

    Kizilcec, Rene F / Makridis, Christos A / Sadowski, Katharine C

    Proceedings of the National Academy of Sciences of the United States of America

    2021  Volume 118, Issue 11

    Abstract: The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and ... ...

    Abstract The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 (
    MeSH term(s) COVID-19 ; Data Science/education ; Democracy ; Education, Distance/economics ; Education, Distance/statistics & numerical data ; Health Policy ; Humans ; Pandemics ; Socioeconomic Factors
    Language English
    Publishing date 2021-03-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2026725118
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Book ; Online: Auditing and Mitigating Cultural Bias in LLMs

    Tao, Yan / Viberg, Olga / Baker, Ryan S. / Kizilcec, Rene F.

    2023  

    Abstract: Culture fundamentally shapes people's reasoning, behavior, and communication. Generative artificial intelligence (AI) technologies may cause a shift towards a dominant culture. As people increasingly use AI to expedite and even automate various ... ...

    Abstract Culture fundamentally shapes people's reasoning, behavior, and communication. Generative artificial intelligence (AI) technologies may cause a shift towards a dominant culture. As people increasingly use AI to expedite and even automate various professional and personal tasks, cultural values embedded in AI models may bias authentic expression. We audit large language models for cultural bias, comparing their responses to nationally representative survey data, and evaluate country-specific prompting as a mitigation strategy. We find that GPT-4, 3.5 and 3 exhibit cultural values resembling English-speaking and Protestant European countries. Our mitigation strategy reduces cultural bias in recent models but not for all countries/territories. To avoid cultural bias in generative AI, especially in high-stakes contexts, we suggest using culture matching and ongoing cultural audits.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 303
    Publishing date 2023-11-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Algorithmic Fairness in Education

    Kizilcec, René F. / Lee, Hansol

    2020  

    Abstract: Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this introduction to ... ...

    Abstract Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this introduction to algorithmic fairness in education, we draw parallels to prior literature on educational access, bias, and discrimination, and we examine core components of algorithmic systems (measurement, model learning, and action) to identify sources of bias and discrimination in the process of developing and deploying these systems. Statistical, similarity-based, and causal notions of fairness are reviewed and contrasted in the way they apply in educational contexts. Recommendations for policy makers and developers of educational technology offer guidance for how to promote algorithmic fairness in education.

    Comment: Forthcoming in W. Holmes & K. Porayska-Pomsta (Eds.), Ethics in Artificial Intelligence in Education, Taylor & Francis
    Keywords Computer Science - Computers and Society ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 004
    Publishing date 2020-07-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Evaluation of Fairness Trade-offs in Predicting Student Success

    Lee, Hansol / Kizilcec, René F.

    2020  

    Abstract: Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algorithmic systems in education. Predictive models may inadvertently introduce ...

    Abstract Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algorithmic systems in education. Predictive models may inadvertently introduce bias in who receives support and thereby exacerbate existing inequities. We examine this issue by building a predictive model of student success based on university administrative records. We find that the model exhibits gender and racial bias in two out of three fairness measures considered. We then apply post-hoc adjustments to improve model fairness to highlight trade-offs between the three fairness measures.

    Comment: FATED (Fairness, Accountability, and Transparency in Educational Data) Workshop at EDM 2020
    Keywords Computer Science - Computers and Society ; Computer Science - Machine Learning
    Publishing date 2020-06-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Artificial intelligence in communication impacts language and social relationships.

    Hohenstein, Jess / Kizilcec, Rene F / DiFranzo, Dominic / Aghajari, Zhila / Mieczkowski, Hannah / Levy, Karen / Naaman, Mor / Hancock, Jeffrey / Jung, Malte F

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 5487

    Abstract: Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences ... ...

    Abstract Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions ("smart replies"), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.
    MeSH term(s) Humans ; Artificial Intelligence ; Interpersonal Relations ; Communication ; Language ; Emotions
    Language English
    Publishing date 2023-04-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-30938-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Publisher Correction: Artificial intelligence in communication impacts language and social relationships.

    Hohenstein, Jess / Kizilcec, Rene F / DiFranzo, Dominic / Aghajari, Zhila / Mieczkowski, Hannah / Levy, Karen / Naaman, Mor / Hancock, Jeffrey / Jung, Malte F

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 16616

    Language English
    Publishing date 2023-10-03
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-43601-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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