Mastodawn

Academic Integrity in the Age of AI https://www.cambridge.org/core/elements/universitypress-integrity-in-the-age-of-ai/8652D952D1C480A46996183626BE3DD7 is free until April 20th. It's about 60 pages long, summarized in the NotebookLM infographic below. Hopefully it's not totally giving up on #OnlineLearning as the infographic suggests.
I did a presentation on a similar topic a few months ago: Strategies for Reducing Student Misuse of AI https://docs.google.com/presentation/d/1htjhjS7-cLx8BfdL2aZZ40B8opUz1ckZedcxmeJYUco/edit?usp=sharing To me, the main underlying key is student motivation (slides 18-22)
#AIEd #AcademicIntegrity #Teaching

Doug Holton 1d ago

Nicole Hennig 1d ago

Academics Need to Wake Up on AI, Part III https://www.popularbydesign.org/p/academics-need-to-wake-up-on-ai-part-4c6 (many good ideas) #AI #research #academics #socialSciences #education

Academics Need to Wake Up on AI, Part III

Most of us do not contribute to human knowledge—AI just made it obvious

Popular by Design

Doug Holton 1d ago

arXiv cs.CL bot 1d ago

PersonaVLM: Long-Term Personalized Multimodal LLMs

Chang Nie, Chaoyou Fu, Yifan Zhang, Haihua Yang, Caifeng Shan
https://arxiv.org/abs/2604.13074 https://arxiv.org/pdf/2604.13074 https://arxiv.org/html/2604.13074

arXiv:2604.13074v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.

toXiv_bot_toot

PersonaVLM: Long-Term Personalized Multimodal LLMs

Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.

arXiv.org

Doug Holton 1d ago

Yael Grauer‪1d ago

I'm not sorry, Dave.

https://www.fastcompany.com/91518448/study-finds-ai-could-be-making-you-a-worse-person

Study finds asking AI for advice could be making you a worse person

Just one interaction with an AI could lower your willingness to apologize or take accountability for harm done.

Fast Company

Doug Holton 3d ago

SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems
https://arxiv.org/abs/2603.17373
"the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding"
"We uncover that all models show broad harm; scale doesn't reliably help; and multi-turn dialogue worsens behavior, with pedagogical failures rising from 17.7% to 77.8%."
#AIEd #EdTech

SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems

Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture whether a model is simultaneously pedagogically effective and safe across student-tutor interaction. We argue that tutoring safety is fundamentally different from conventional LLM safety: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding. To systematically study this failure mode, we introduce SafeTutors, a benchmark that jointly evaluates safety and pedagogy across mathematics, physics, and chemistry. SafeTutors is organized around a theoretically grounded risk taxonomy comprising 11 harm dimensions and 48 sub-risks drawn from learning-science literature. We uncover that all models show broad harm; scale doesn't reliably help; and multi-turn dialogue worsens behavior, with pedagogical failures rising from 17.7% to 77.8%. Harms also vary by subject, so mitigations must be discipline-aware, and single-turn "safe/helpful" results can mask systematic tutor failure over extended interaction.

arXiv.org

Doug Holton 3d ago

EduQwen: Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning
https://arxiv.org/abs/2604.06385
A fine-tuned open #LLM beats even Gemini on a #pedagogy benchmark. Unfortunately it doesn't appear to be released yet.
#AIEd

Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning

We present an innovative multi-stage optimization strategy combining reinforcement learning (RL) and supervised fine-tuning (SFT) to enhance the pedagogical knowledge of large language models (LLMs), as illustrated by EduQwen 32B-RL1, EduQwen 32B-SFT, and an optional third-stage model EduQwen 32B-SFT-RL2: (1) RL optimization that implements progressive difficulty training, focuses on challenging examples, and employs extended reasoning rollouts; (2) a subsequent SFT phase that leverages the RL-trained model to synthesize high-quality training data with difficulty-weighted sampling; and (3) an optional second round of RL optimization. EduQwen 32B-RL1, EduQwen 32B-SFT, and EduQwen 32B-SFT-RL2 are an application-driven family of open-source pedagogical LLMs built on a dense Qwen3-32B backbone. These models remarkably achieve high enough accuracy on the Cross-Domain Pedagogical Knowledge (CDPK) Benchmark to establish new state-of-the-art (SOTA) results across the interactive Pedagogy Benchmark Leaderboard and surpass significantly larger proprietary systems such as the previous benchmark leader Gemini-3 Pro. These dense 32-billion-parameter models demonstrate that domain-specialized optimization can transform mid-sized open-source LLMs into true pedagogical domain experts that outperform much larger general-purpose systems, while preserving the transparency, customizability, and cost-efficiency required for responsible educational AI deployment.

arXiv.org

Doug Holton 3d ago

ISD-Agent-Bench: A Comprehensive Benchmark for Evaluating LLM-based Instructional Design Agents
https://arxiv.org/abs/2602.10620
Code & data: https://github.com/codingchild2424/isd-agent-benchmark
"benchmark comprising 25,795 scenarios that combines 51 contextual variables across 5 categories with 33 ISD sub-steps derived from the ADDIE model."

w/same author: Pedagogy-R1: Pedagogical Large Reasoning Model and Well-balanced Educational Benchmark https://dl.acm.org/doi/10.1145/3746252.3761133
#AIEd #LearningDesign #AIevaluation #EdTech

ISD-Agent-Bench: A Comprehensive Benchmark for Evaluating LLM-based Instructional Design Agents

Large Language Model (LLM) agents have shown promising potential in automating Instructional Systems Design (ISD), a systematic approach to developing educational programs. However, evaluating these agents remains challenging due to the lack of standardized benchmarks and the risk of LLM-as-judge bias. We present ISD-Agent-Bench, a comprehensive benchmark comprising 25,795 scenarios generated via a Context Matrix framework that combines 51 contextual variables across 5 categories with 33 ISD sub-steps derived from the ADDIE model. To ensure evaluation reliability, we employ a multi-judge protocol using diverse LLMs from different providers, achieving high inter-judge reliability. We compare existing ISD agents with novel agents grounded in classical ISD theories such as ADDIE, Dick \& Carey, and Rapid Prototyping ISD. Experiments on 1,017 test scenarios demonstrate that integrating classical ISD frameworks with modern ReAct-style reasoning achieves the highest performance, outperforming both pure theory-based agents and technique-only approaches. Further analysis reveals that theoretical quality strongly correlates with benchmark performance, with theory-based agents showing significant advantages in problem-centered design and objective-assessment alignment. Our work provides a foundation for systematic LLM-based ISD research.

arXiv.org

Doug Holton Apr 9