arXiv subjects

Amrita Ganguly

Publications and source records attributed to Amrita Ganguly.

10 recordsLinked to original sources

arXiv2026

A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education

With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning are significant. At the same time, instructors have also been forced to address the use of GenAI as students have started to use it for a range of functions. Currently, comparative analysis of guidance provided by institutions and its uptake in instruction is lacking. In this paper we bridge this gap by comparing institutional and computing course level guidance to better understand this terrain. We utilize secondary analysis of institutional and course syllabi guidelines from higher education institutions in the U.S. classified as research-intensive. Our findings reveal that although institutional guidance is more pro-use, at the course-level the uptake is still guarded. We discuss the implications and propose an instructor-centered framework to guide future adoption of GenAI.

cs.CY↗

arXiv2026

Uncovering Students' Mental Models of Generative Artificial Intelligence

In this paper we present a study of students' mental models of generative AI (GenAI). A student's mental model of GenAI influences not only how they perceive the technology's capabilities and limitations but also how they choose to integrate it into their academic work. Whether they view it as a collaborative partner, a shortcut to complete tasks, or something in between, depends on how they conceptualize its use. This study addresses the following questions: (I) What mental models do undergraduate students hold about GenAI? and (II) What aspects of conceptual knowledge - declarative, procedural, and conditional - are present in these mental models? Sixty-four concept maps were collected from students enrolled in a course on technology ethics. Students were asked to construct concept maps representing their understanding of GenAI use. The concept maps were analyzed using a structured codebook and the analysis revealed five categories of mental models: technical process based, educational tool based, transition model, consequence aware model, integrated model. Declarative knowledge was most dominant across maps, suggesting that students largely understood GenAI primarily at a surface level - knowing its names, tools, and applications but demonstrate limited procedural understanding of how it works and limited conditional knowledge about when and why it should or should not be used. By identifying students' mental models, we can improve students' AI literacy by designing curriculum and guidelines that improve cognition while ensuring responsible and ethical use.

cs.CY↗

arXiv2025

Generative Artificial Intelligence for Academic Research: Evidence from Guidance Issued for Researchers by Higher Education Institutions in the United States

The recent development and use of generative AI (GenAI) has signaled a significant shift in research activities such as brainstorming, proposal writing, dissemination, and even reviewing. This has raised questions about how to balance the seemingly productive uses of GenAI with ethical concerns such as authorship and copyright issues, use of biased training data, lack of transparency, and impact on user privacy. To address these concerns, many Higher Education Institutions (HEIs) have released institutional guidance for researchers. To better understand the guidance that is being provided we report findings from a thematic analysis of guidelines from thirty HEIs in the United States that are classified as R1 or 'very high research activity.' We found that guidance provided to researchers: (1) asks them to refer to external sources of information such as funding agencies and publishers to keep updated and use institutional resources for training and education; (2) asks them to understand and learn about specific GenAI attributes that shape research such as predictive modeling, knowledge cutoff date, data provenance, and model limitations, and educate themselves about ethical concerns such as authorship, attribution, privacy, and intellectual property issues; and (3) includes instructions on how to acknowledge sources and disclose the use of GenAI, how to communicate effectively about their GenAI use, and alerts researchers to long term implications such as over reliance on GenAI, legal consequences, and risks to their institutions from GenAI use. Overall, guidance places the onus of compliance on individual researchers making them accountable for any lapses, thereby increasing their responsibility.

cs.CY↗

arXiv2024

MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting

In this paper, we present MasonTigers' participation in SemEval-2024 Task 10, a shared task aimed at identifying emotions and understanding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for Hindi-English code-mixed dialogues, emotion flip reasoning for Hindi-English code-mixed dialogues, and emotion flip reasoning for English dialogues. Our team, MasonTigers, contributed to each subtask, focusing on developing methods for accurate emotion recognition and reasoning. By leveraging our approaches, we attained impressive F1-scores of 0.78 for the first task and 0.79 for both the second and third tasks. This performance not only underscores the effectiveness of our methods across different aspects of the task but also secured us the top rank in the first and third subtasks, and the 2nd rank in the second subtask. Through extensive experimentation and analysis, we provide insights into our system's performance and contributions to each subtask.

cs.CL↗

arXiv2024

MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness

This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.

cs.CL↗

arXiv2024

MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection

This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.

cs.CL↗

arXiv2024

MasonTigers at SemEval-2024 Task 9: Solving Puzzles with an Ensemble of Chain-of-Thoughts

Our paper presents team MasonTigers submission to the SemEval-2024 Task 9 - which provides a dataset of puzzles for testing natural language understanding. We employ large language models (LLMs) to solve this task through several prompting techniques. Zero-shot and few-shot prompting generate reasonably good results when tested with proprietary LLMs, compared to the open-source models. We obtain further improved results with chain-of-thought prompting, an iterative prompting method that breaks down the reasoning process step-by-step. We obtain our best results by utilizing an ensemble of chain-of-thought prompts, placing 2nd in the word puzzle subtask and 13th in the sentence puzzle subtask. The strong performance of prompted LLMs demonstrates their capability for complex reasoning when provided with a decomposition of the thought process. Our work sheds light on how step-wise explanatory prompts can unlock more of the knowledge encoded in the parameters of large models.

cs.CL↗

arXiv2024

Collaborative Job Seeking for People with Autism: Challenges and Design Opportunities

Successful job search results from job seekers' well-shaped social communication. While well-known differences in communication exist between people with autism and neurotypicals, little is known about how people with autism collaborate with their social surroundings to strive in the job market. To better understand the practices and challenges of collaborative job seeking for people with autism, we interviewed 20 participants including applicants with autism, their social surroundings, and career experts. Through the interviews, we identified social challenges that people with autism face during their job seeking; the social support they leverage to be successful; and the technological limitations that hinder their collaboration. We designed four probes that represent major collaborative features found from the interviews--executive planning, communication, stage-wise preparation, and neurodivergent community formation--and discussed their potential usefulness and impact through three focus groups. We provide implications regarding how our findings can enhance collaborative job seeking experiences for people with autism through new designs.

cs.HC↗

arXiv2024

MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles

The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.

cs.CL↗

arXiv2024

MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification

The task of identifying public opinions on social media, particularly regarding climate activism and the detection of hate events, has emerged as a critical area of research in our rapidly changing world. With a growing number of people voicing either to support or oppose to climate-related issues - understanding these diverse viewpoints has become increasingly vital. Our team, MasonPerplexity, participates in a significant research initiative focused on this subject. We extensively test various models and methods, discovering that our most effective results are achieved through ensemble modeling, enhanced by data augmentation techniques like back-translation. In the specific components of this research task, our team achieved notable positions, ranking 5th, 1st, and 6th in the respective sub-tasks, thereby illustrating the effectiveness of our approach in this important field of study.

cs.CL↗