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Carolyn Penstein Rosé - Carnegie Mellon University. Pittsburgh, PA, US

Carolyn Penstein Rosé

Professor | Carnegie Mellon University


Carolyn Penstein Rosé's research looks to understand and improve human conversation through computer systems.


Carolyn Penstein Rosé's research looks to understand human conversation and use this understanding to build computer systems that can improve the efficacy of conversation between people, and between people and computers. In pursuit of these goals, she utilizes approaches from computational discourse analysis and text mining, conversational agents and computer supported collaborative learning. Her work is grounded in the fields of language technologies and human-computer interaction.

Areas of Expertise (7)

Language Technologies

Human Conversation

Text Mining

Human-Computer Interaction

Computational Discourse Analysis

Conversational Agents

Computer Supported Collaborative Learning

Media Appearances (4)

The AI company Elon Musk cofounded just released a 'groundbreaking' tool that can automatically mimic human writing — here's how stunned developers are experimenting with it so far

Business Insider  online


"Historically, natural language generation systems have lacked some nuance," said Carolyn Rose, a professor at Carnegie Mellon University's Language Technologies Institute. But GPT-3 seems different. Based on early reactions, GPT-3 has blown past existing models thanks to its massive dataset and its use of 175 billion parameters — rules the algorithm relies on to decide which word should come next to mimic conversational English. By comparison, the previous version, GPT-2, utilized 1.5 billion parameters, and the next most powerful model — from Microsoft — has 17 billion parameters.

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Elon Musk-Backed AI Company Launches New Tool that Writes Naturally Like Humans

Tech Times  


Carnegie Mellon University's Language Technologies Institute Professor Carolyn Rose told Business Insider that while natural language generation systems have historically "lacked some nuance," GPT-3 seems different.

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Online Classes Get a Missing Piece: Teamwork

EdSurge  online


Carolyn Rosé, an associate professor in the Human-Computer Interaction Institute at Carnegie Mellon University, has been exploring ways to add social engagement to MOOCs since 2013. She and fellow researchers developed Bazaar, the tool that California community colleges will test in online statistics courses this fall.

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How Conversation Impacts Learning

Class Central  online


Prof. Carolyn Rosé’s research focuses on modeling conversations between students in learning contexts to find out what it is about conversations that makes them valuable for learning. To study conversations, she uses text mining, machine learning, and computational discourse analysis. With this new understanding, her goal is to design interventions to support learning in online settings.

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Invited Talk 1: Carolyn Penstein Rosé MRI13 Interview of Carolyn Rose Medical Dialogue Virtual Roundtable Carolyn Penstein Rosé



Industry Expertise (2)


Computer Software

Education (3)

Carnegie Mellon University: M.S., Computational Linguistics 1994

Carnegie Mellon University:: Ph.D., Language and Information Technologies 1997

University of California at Irvine: B.S., Information and Computer Science 1992

Articles (5)

Examining computational thinking processes in modeling unstructured data

Education and Information Technologies

2023 As artificial intelligence (AI) technologies are increasingly pervasive in our daily lives, the need for students to understand the working mechanisms of AI technologies has become more urgent. Data modeling is an activity that has been proposed to engage students in reasoning about the working mechanism of AI technologies. While Computational thinking (CT) has been conceptualized as critical processes that students engage in during data modeling, much remains unexplored regarding how students created features from unstructured data to develop machine learning models. In this study, we examined high school students’ patterns of iterative model development and themes of CT processes in iterative model development. Twenty-eight students from a journalism class engaged in refining machine learning models iteratively for classifying negative and positive reviews of ice cream stores.

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Nine elements for robust collaborative learning analytics: A constructive collaborative critique

International Journal of Computer-Supported Collaborative Learning

2023 This editorial represents a collaborative effort between the current co-editors-in-chief of the International Journal of Computer-Supported Collaborative Learning (ijCSCL) and the recent co-editor-in-chief of the Journal of Learning Analytics (JLA), Alyssa Wise, who is also a member of the ijC (LA) have made a presence in ijCSCL. This issue in particular comprises four full articles within this scope, in addition to a timely exposition on Collaborative Learning from an ethics perspective. Thus, it is high time to bring in a voice of leadership from the LA community together with those of the CSCL community to think together about the intersection of work across the two fields.

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High school students’ data modeling practices and processes: From modeling unstructured data to evaluating automated decisions

Learning, Media and Technology

2023 It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies.

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Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

British Journal of Educational Technology

2023 Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace‐based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace‐based approach to study SSRL, there remains a paucity of evidence on how trace‐based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes

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Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

Transactions of the Association for Computational Linguistics

2022 Natural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: Have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail? We conceptualize the long tail using macro-level dimensions (underrepresented genres, topics, etc.), and perform a qualitative meta-analysis of 100 representative papers on transfer learning research for NLU. Our analysis asks three questions: (i) Which long tail dimensions do transfer learning studies target? (ii) Which properties of adaptation methods help improve performance on the long tail? (iii) Which methodological gaps have greatest negative impact on long tail performance?

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