Understanding Human-AI Trust in Education

As AI chatbots become increasingly integrated in education, students are turning to these systems for guidance, feedback, and information. However, the anthropomorphic characteristics of these chatbots create ambiguity regarding whether students develop trust toward them as they would a human peer or instructor, based in interpersonal trust, or as they would any other piece of technology, based in technology trust. This ambiguity presents theoretical challenges, as interpersonal trust models may inappropriately ascribe human intentionality and morality to AI, while technology trust models were developed for non-social technologies, leaving their applicability to anthropomorphic systems unclear. To address this gap, we investigate how human-like and system-like trusting beliefs comparatively influence students' perceived enjoyment, trusting intention, behavioral intention to use, and perceived usefulness of an AI chatbot - factors associated with students' engagement and learning outcomes. Through partial least squares structural equation modeling, we found that human-like and system-like trust significantly influenced student perceptions, with varied effects. Human-like trust more strongly predicted trusting intention, while system-like trust better predicted behavioral intention and perceived usefulness. Both had similar effects on perceived enjoyment. Given the partial explanatory power of each type of trust, we propose that students develop a distinct form of trust with AI chatbots (human-AI trust) that differs from human-human and human-technology models of trust. Our findings highlight the need for new theoretical frameworks specific to human-AI trust and offer practical insights for fostering appropriately calibrated trust, which is critical for the effective adoption and pedagogical impact of AI in education.

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour

This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated by human annotators for both general manipulation and specific manipulation tactics. Our research reveals three key findings. First, Large Language Models (LLMs) can be manipulative when explicitly instructed, with annotators identifying manipulation in approximately 84\% of such conversations. Second, even when only instructed to be ``persuasive'' without explicit manipulation prompts, LLMs frequently default to controversial manipulative strategies, particularly gaslighting and fear enhancement. Third, small fine-tuned open source models, such as BERT+BiLSTM have a performance comparable to zero-shot classification with larger models like Gemini 2.5 pro in detecting manipulation, but are not yet reliable for real-world oversight. Our work provides important insights for AI safety research and highlights the need of addressing manipulation risks as LLMs are increasingly deployed in consumer-facing applications.

Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications

As generative AI tools become increasingly integrated into higher education, understanding how students interact with and perceive these technologies is essential for responsible and effective adoption. This study evaluates the use of the Educational AI Hub, an AI-powered learning framework, in undergraduate civil and environmental engineering courses at a large R1 public university. Using a mixed-methods approach that combines pre- and post-surveys, system usage logs, and qualitative analysis of the open-ended prompts and questions students posed to the AI chatbot, the research explores students' perceptions of trust, ethical concerns, usability, and learning outcomes. Findings reveal that students appreciated the AI assistant for its convenience and comfort, with nearly half reporting greater ease in using the AI tool compared to seeking help from instructors or teaching assistants. The tool was seen as most helpful for completing homework and understanding course concepts, though perceptions of its instructional quality were mixed. Ethical concerns emerged as a key barrier to full engagement: while most students viewed AI use as ethically acceptable, many expressed uncertainties about institutional policies and apprehension about potential academic misconduct. This study contributes to the growing body of research on AI in education by highlighting the importance of usability, policy clarity, and faculty guidance in fostering meaningful AI engagement. The findings suggest that while students are ready to embrace AI as a supplement to human instruction, thoughtful integration and transparent institutional frameworks are critical for ensuring student confidence, trust, and learning effectiveness.

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy

As generative AI (GenAI) becomes increasingly widespread, it is crucial to equip users, particularly vulnerable populations such as older adults (65 and older), with the knowledge to understand its benefits and potential risks. Older adults often exhibit greater reservations about adopting emerging technologies and require tailored literacy support. Using a mixed methods approach, this study examines strategies for delivering GenAI literacy to older adults through a chatbot named Litti, evaluating its impact on their AI literacy (knowledge, safety, and ethical use). The quantitative data indicated a trend toward improved AI literacy, though the results were not statistically significant. However, qualitative interviews revealed diverse levels of familiarity with generative AI and a strong desire to learn more. Findings also show that while Litti provided a positive learning experience, it did not significantly enhance participants' trust or sense of safety regarding GenAI. This exploratory case study highlights the challenges and opportunities in designing AI literacy education for the rapidly growing older adult population.

Urania: Differentially Private Insights into AI Use

We introduce $Urania$, a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy (DP) guarantees. The framework employs a private clustering mechanism and innovative keyword extraction methods, including frequency-based, TF-IDF-based, and LLM-guided approaches. By leveraging DP tools such as clustering, partition selection, and histogram-based summarization, $Urania$ provides end-to-end privacy protection. Our evaluation assesses lexical and semantic content preservation, pair similarity, and LLM-based metrics, benchmarking against a non-private Clio-inspired pipeline (Tamkin et al., 2024). Moreover, we develop a simple empirical privacy evaluation that demonstrates the enhanced robustness of our DP pipeline. The results show the framework's ability to extract meaningful conversational insights while maintaining stringent user privacy, effectively balancing data utility with privacy preservation.

SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs

Recent calls for pluralistic alignment of Large Language Models (LLMs) encourage adapting models to diverse user preferences. However, most prior work on personalized reward models heavily rely on additional identity information, such as demographic details or a predefined set of preference categories. To this end, we introduce SynthesizeMe, an approach to inducing synthetic user personas from user interactions for personalized reward modeling. SynthesizeMe first generates and verifies reasoning to explain user preferences, then induces synthetic user personas from that reasoning, and finally filters to informative prior user interactions in order to build personalized prompts for a particular user. We show that using SynthesizeMe induced prompts improves personalized LLM-as-a-judge accuracy by 4.4% on Chatbot Arena. Combining SynthesizeMe derived prompts with a reward model achieves top performance on PersonalRewardBench: a new curation of user-stratified interactions with chatbots collected from 854 users of Chatbot Arena and PRISM.

Privacy and Security Threat for OpenAI GPTs

Large language models (LLMs) demonstrate powerful information handling capabilities and are widely integrated into chatbot applications. OpenAI provides a platform for developers to construct custom GPTs, extending ChatGPT's functions and integrating external services. Since its release in November 2023, over 3 million custom GPTs have been created. However, such a vast ecosystem also conceals security and privacy threats. For developers, instruction leaking attacks threaten the intellectual property of instructions in custom GPTs through carefully crafted adversarial prompts. For users, unwanted data access behavior by custom GPTs or integrated third-party services raises significant privacy concerns. To systematically evaluate the scope of threats in real-world LLM applications, we develop three phases instruction leaking attacks target GPTs with different defense level. Our widespread experiments on 10,000 real-world custom GPTs reveal that over 98.8% of GPTs are vulnerable to instruction leaking attacks via one or more adversarial prompts, and half of the remaining GPTs can also be attacked through multiround conversations. We also developed a framework to assess the effectiveness of defensive strategies and identify unwanted behaviors in custom GPTs. Our findings show that 77.5% of custom GPTs with defense strategies are vulnerable to basic instruction leaking attacks. Additionally, we reveal that 738 custom GPTs collect user conversational information, and identified 8 GPTs exhibiting data access behaviors that are unnecessary for their intended functionalities. Our findings raise awareness among GPT developers about the importance of integrating specific defensive strategies in their instructions and highlight users' concerns about data privacy when using LLM-based applications.

A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs

The advancement of Large Language Models (LLMs) has led to significant improvements in various service domains, including search, recommendation, and chatbot applications. However, applying state-of-the-art (SOTA) research to industrial settings presents challenges, as it requires maintaining flexible conversational abilities while also strictly complying with service-specific constraints. This can be seen as two conflicting requirements due to the probabilistic nature of LLMs. In this paper, we propose our approach to addressing this challenge and detail the strategies we employed to overcome their inherent limitations in real-world applications. We conduct a practical case study of a conversational agent designed for the e-commerce domain, detailing our implementation workflow and optimizations. Our findings provide insights into bridging the gap between academic research and real-world application, introducing a framework for developing scalable, controllable, and reliable AI-driven agents.

From Chat Logs to Collective Insights: Aggregative Question Answering

Conversational agents powered by large language models (LLMs) are rapidly becoming integral to our daily interactions, generating unprecedented amounts of conversational data. Such datasets offer a powerful lens into societal interests, trending topics, and collective concerns. Yet, existing approaches typically treat these interactions as independent and miss critical insights that could emerge from aggregating and reasoning across large-scale conversation logs. In this paper, we introduce Aggregative Question Answering, a novel task requiring models to reason explicitly over thousands of user-chatbot interactions to answer aggregative queries, such as identifying emerging concerns among specific demographics. To enable research in this direction, we construct a benchmark, WildChat-AQA, comprising 6,027 aggregative questions derived from 182,330 real-world chatbot conversations. Experiments show that existing methods either struggle to reason effectively or incur prohibitive computational costs, underscoring the need for new approaches capable of extracting collective insights from large-scale conversational data.

MEDAL: A Framework for Benchmarking LLMs as Multilingual Open-Domain Chatbots and Dialogue Evaluators

As the capabilities of chatbots and their underlying LLMs continue to dramatically improve, evaluating their performance has increasingly become a major blocker to their further development. A major challenge is the available benchmarking datasets, which are largely static, outdated, and lacking in multilingual coverage, limiting their ability to capture subtle linguistic and cultural variations. This paper introduces MEDAL, an automated multi-agent framework for generating, evaluating, and curating more representative and diverse open-domain dialogue evaluation benchmarks. Our approach leverages several state-of-the-art LLMs to generate user-chatbot multilingual dialogues, conditioned on varied seed contexts. A strong LLM (GPT-4.1) is then used for a multidimensional analysis of the performance of the chatbots, uncovering noticeable cross-lingual performance differences. Guided by this large-scale evaluation, we curate a new meta-evaluation multilingual benchmark and human-annotate samples with nuanced quality judgments. This benchmark is then used to assess the ability of several reasoning and non-reasoning LLMs to act as evaluators of open-domain dialogues. We find that current LLMs struggle to detect nuanced issues, particularly those involving empathy and reasoning.