Panos Ipeirotis

Special Issue of Production and Operations Management on "Generative AI (GenAI) and Agentic AI at the Marketing–Operations Interface." Guest edited by Roland Rust, Ming-Hui Huang, Shane Wang, Praveen Kopalle. Commitment to Timeliness   Given the rapid pace of research and practice in this domain, the editorial team is committed to quick turnaround and timely decisions. A paper submitted to the special issue will be processed right away. Authors are encouraged to submit as soon as they are ready. Our goal is to ensure that accepted papers are published swiftly, with the special issue scheduled for print publication in early 2027 to maximize its relevance and impact.   First-round submission deadline: June 30, 2026   First-round decisions: August 31, 2026   Revised submission deadline: November 15, 2026   Final decisions: December 31, 2026

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I’m happy to share our recent journal article presenting FAIR Epigraphy, a community-driven framework for interoperable epigraphic data. The paper addresses the fragmentation of digital inscription data by proposing standardised controlled vocabularies aligned with international authorities and implemented as linked open data. These vocabularies—already available as RDF with stable URIs—enable cross-database querying and stronger connections between epigraphy and wider cultural heritage resources, including Wikidata. This work supports FAIR principles and opens new pathways for large-scale, collaborative, and computational research on the ancient world. 🔗 Read the article: https://lnkd.in/eBrmBxy9 #ResearchPublication #DigitalEpigraphy #FAIRData #LinkedOpenData #SemanticWeb #CulturalHeritage

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As someone deeply engaged with both academia and industry, I find it inspiring to see rigorous thinking brought to the evaluation of AI agents. Simona Temelkova Nikolić has authored an article that captures both the challenges and opportunities in this space. Highly recommended for colleagues navigating AI and related fields.

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Springer Nature book on AI ethics riddled with fake citations even as the publisher announces an OA reviews journal — a known magnet for AI slop. https://lnkd.in/e_hEYTuS

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AI on Review: How Large Language Models Are Reshaping Peer Review The Peer Review Crunch and New "Reviewer Duties" Peer review is the backbone of scientific quality control, ensuring that research findings are vetted for accuracy and significance before publication (pmc.ncbi.nlm.nih.gov). In fast-moving fields like machine learning and computer vision, top conferences function much like journals - and the integrity of science depends on rigorous peer evaluation. However, the system is straining under an avalanche of submissions. Major AI conferences now routinely receive well over 10,000 papers, a surge that has stretched the reviewer pool to its limits (arxiv.org). This deluge has led to radical policy changes: some conferences now essentially conscript all submitting authors into service as reviewers. For example, ICLR 2025 explicitly warned authors that any paper without at least one author signed up to review would be desk-rejected (reddit.com). NeurIPS and others have similarly pleaded that "all authors help with reviewing, if asked," to tackle the reviewer shortage. https://lnkd.in/d7KDP99K

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Pleased to share this new publication in the Journal of Open Research Software, part of an ongoing collaboration between the CulturePlex Lab and the Donostia International Physics Center (DIPC). Titled WikiTextGraph, we present a Python tool where it extracts text and graph of Wikipedia across multiple languages (English, Spanish, Greek, Dutch, German, Hindi, Vietnamese, Basque, Polish, and Italian). #Python #OpenSource #DataScience #KnowledgeGraphs #WikiTextGraph

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Ohio State University's AI Fluency initiative (https://lnkd.in/etB8EyMB) sets an ambitious goal: every undergraduate will graduate with practical generative AI skills by 2029. Let's unpack this: ✅ Strong Points: ▪️Bold, clearly articulated institutional vision. ▪️Multi-layered integration (Gen Ed courses, discipline-specific modules, innovation opportunities). ▪️Dedicated resources and infrastructure (faculty support via Drake Institute). ▪️Commitment to hands-on innovation (hackathons, prototyping workshops). ⚠️ Areas of Concern: ▪️Faculty workload: Realistic expectations for widespread course redesign and adoption? ▪️Standardization & Assessment: What does "AI fluency" mean in practice across diverse fields? ▪️Curriculum Overload: Risk of student pushback if mandatory modules aren't streamlined or clearly valuable. ▪️Equity and Faculty Incentives: What's the motivation for faculty, especially those skeptical or hesitant about AI integration? What if they're not on board with using AI in the classroom, or it doesn't make sense for their course? ▪️Soft Skills and Ethical Frameworks: How thoroughly are ethics, interpretability, and responsible AI practices embedded? OSU's vision is exciting, but effective execution will be key. I'm curious: What does successful "AI fluency" look like in your discipline? And how should institutions balance ambition with realistic implementation? Let's talk!

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This study used impressive, theory-informed coding and analyses of digital trace data to reveal clusters of students who reliably differed on prior knowledge, motivation, and performance. To me, this demonstrates how strong validity arguments can be made about the coding of digital trace data. https://lnkd.in/ecMxe7Ht

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Geraldine McBride, CEO of MyWave.AI explains how industry process models and intelligent data orchestration make the company's agentic AI hallucination-free and easy to customize and deploy. The technology can span on-premises and cloud ERP, a big reason SAP partnered with MyWave on agent-based automation for its Rise and Grow services for migrating to S/4HANA Cloud. It's all in the latest episode of the Enterprise Apps Unpacked podcast out this morning. https://lnkd.in/ebX_UqQr

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Happy to share that our paper on chatbot gender has been accepted at MIS Quarterly, a premier journal in Information Systems. This work, coauthored with Professors Yiting Guo, Sean (Xin) Xu, and Ximing Yi, examines the real-world deployment of a voice-only debt-collection bot. We study whether the perceived gender of a voicebot influences repayment intention. The key finding: simply changing the bot’s voice from male to female — without altering the script in any way — increases the odds of repayment intention by 28.3%. Why? We find that a female voice is more effective at reducing tension in high-stress service interactions, which in turn improves service outcomes. A small design change. A meaningful behavioral impact.

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