René F Kizilcec
Biographic Data
| ID | 5120560 |
|---|---|
| NAME | René F Kizilcec |
| GIVEN NAMES | René F |
| FAMILY NAME | Kizilcec |
| SIGNATURE | KIZILCEC R F |
| AFFILIATIONS | Cornell University |
| ORCID | 0000-0001-6283-5546 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 11 |
| WORKS AS AUTHOR | 15 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Advancing 21st-Century Professional Competencies with Learning Analytics in the Age of Generative AI
The rise of generative artificial intelligence (GenAI) and accelerated globalization have necessitated a fundamental recalibration of higher education to prioritize domain-agnostic, 21st-century professional competencies. While institutional commitment to these skills is high, their systematic integration into the curriculum and evaluation remains fragmented, highlighting a critical gap between traditional academic success metrics and demonstrate…
Cultural bias and cultural alignment of large language models
Culture fundamentally shapes people’s reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people’s authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI’s GPT-4o/4-…
Are Mooc learning designs culturally inclusive (enough)
A systemic model of academic (mis)conduct to curb cheating in higher education
The life cycle of large language models in education
Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM‐based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities.…
Showing authentic examples of academic and career trajectories to influence college students' career exploration
The social consequences of Machine Allocation Behavior
From emergency to sustainable online learning
Standing up to problematic content on social media
Problematic content on social media can be countered through objections raised by other community members. While intended to deter offenses, objections can influence the surrounding audience observing the interaction, leading to their collective approval or disapproval. The results of an experiment manipulating seven types of objections against common types of offenses indicate audiences’ support for objections that implore via appeals and disapp…
Beyond one-size-fits-all in Moocs
Combating sharenting
Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses
How Much Information?
The rising prevalence of algorithmic interfaces, such as curated feeds in online news, raises new questions for designers, scholars, and critics of media. This work focuses on how transparent design of algorithmic interfaces can promote awareness and foster trust. A two-stage process of how transparency affects trust was hypothesized drawing on theories of information processing and procedural justice. In an online field experiment, three levels …
Social robots and virtual agents as lecturers for video instruction
Deconstructing disengagement
As MOOCs grow in popularity, the relatively low completion rates of learners has been a central criticism. This focus on completion rates, however, reflects a monolithic view of disengagement that does not allow MOOC designers to target interventions or develop adaptive course features for particular subpopulations of learners. To address this, we present a simple, scalable, and informative classification method that identifies a small number of …
Deconstructing disengagement
As MOOCs grow in popularity, the relatively low completion rates of learners has been a central criticism. This focus on completion rates, however, reflects a monolithic view of disengagement that does not allow MOOC designers to target interventions or develop adaptive course features for particular subpopulations of learners. To address this, we present a simple, scalable, and informative classification method that identifies a small number of …
Social robots and virtual agents as lecturers for video instruction
How Much Information?
The rising prevalence of algorithmic interfaces, such as curated feeds in online news, raises new questions for designers, scholars, and critics of media. This work focuses on how transparent design of algorithmic interfaces can promote awareness and foster trust. A two-stage process of how transparency affects trust was hypothesized drawing on theories of information processing and procedural justice. In an online field experiment, three levels …
Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses
Combating sharenting
Beyond one-size-fits-all in Moocs
The social consequences of Machine Allocation Behavior
From emergency to sustainable online learning
Standing up to problematic content on social media
Problematic content on social media can be countered through objections raised by other community members. While intended to deter offenses, objections can influence the surrounding audience observing the interaction, leading to their collective approval or disapproval. The results of an experiment manipulating seven types of objections against common types of offenses indicate audiences’ support for objections that implore via appeals and disapp…
Cultural bias and cultural alignment of large language models
Culture fundamentally shapes people’s reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people’s authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI’s GPT-4o/4-…
Are Mooc learning designs culturally inclusive (enough)
A systemic model of academic (mis)conduct to curb cheating in higher education
The life cycle of large language models in education
Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM‐based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities.…
Showing authentic examples of academic and career trajectories to influence college students' career exploration
Advancing 21st-Century Professional Competencies with Learning Analytics in the Age of Generative AI
The rise of generative artificial intelligence (GenAI) and accelerated globalization have necessitated a fundamental recalibration of higher education to prioritize domain-agnostic, 21st-century professional competencies. While institutional commitment to these skills is high, their systematic integration into the curriculum and evaluation remains fragmented, highlighting a critical gap between traditional academic success metrics and demonstrate…
Psychology (13 works) · Computer Science (12 works) · Mathematics education (6 works) · Social Psychology (6 works) · Online Learning and Analytics (5 works) · Sociology (5 works) · Multimedia (4 works) · Online and Blended Learning (4 works) · Political science (4 works) · Engineering (3 works)