July 29 ~ 30, 2026, Virtual Conference
Achim von Heynitz, University of Erfurt, Germany
Traditional Results-Based Management (RBM) frameworks in international development organizations remain inherently constrained by linear control cycles and retrospective learning; managerial incentives are heavily biased toward short-term output delivery and disbursement over long-term sustainable outcomes. To address these structural misalignments, this paper introduces a conceptual paradigm shift centered on a human-in-the-loop Agentic AI Orchestration Hub. We propose three interlinked, mutually reinforcing algorithmic innovations that transform the project life cycle: (1) Algorithmic Triangulation to internalize accountability by continuously synthesizing output delivery, risk exposure, and assumption validity; (2) an Impact Futures Market (IFM) that converts future probabilistic outcomes into present-day decision signals via Tradable Impact Assets (TIAs) under adversarial EvalAgent validation; and (3) a Retrospective Impact Market (RIM) backed by contingent, flexible post-implementation credit lines to capture and value emergent, unplanned results. This distributed agent-supporfted framework transitions project management from deterministic plan execution to continuous, adaptable outcome stewardship
Agentic AI, Teleological Orchestration in Results-Based Management (RBM), Tradable Impact Assets (TIAs), Impact Futures Markets, Multilateral Development Banks.
Alan Chickinsky, Life Senior Member, IEEE
Contemporary AI language models are increasingly deployed in safety-critical contexts, yet they remain prone to generating dangerous or factually incorrect outputs. Documented examples include AI-generated recipes suggesting the use of chlorine-producing ingredient combinations [1]. Rather than acknowledging these as model errors, developers have characterized them as “hallucinations”—a term that obscures the underlying technical causes. This paper investigates the structural and methodological sources of hallucination in neural network models, examines three predominant training paradigms, and argues that incomplete training data, flawed train–test partitioning, and the inaccessibility of expert tacit knowledge are root causes of unreliable AI outputs. Recommendations for more rigorous training methodologies are proposed.
Artificial Intelligence, Hallucinations, Neural Networks.
Hengwu Li, Shandong University of Finance and Economics, China
In the era of artificial intelligence (AI), there is an urgent societal demand for digital-intelligent talents. Innovative curriculum design serves as a vital link in cultivating such talents. This paper first elaborates on the theoretical basis for the innovative curriculum design of digital-intelligent talent cultivation. Then it discusses the concepts, ideas and routes of hybrid learning innovative design targeting key teaching problems. Taking the course Algorithm Design and Analysis as an example, it explores the implementation approaches of hybrid learning innovation from five aspects: content reconstruction, environment construction, process reshaping, method innovation and evaluation reform. Finally, it demonstrates the practical application and effectiveness of hybrid learning innovation.
Digital-Intelligent Talents; Talent Cultivation; Innovative Design; Innovative Implementation; Innovative Practice.
Katerina Katsamba and Waldemar Pfoertsch, University of Limassol (CIIM), Cyprus
Human-centered artificial intelligence is usually framed as a design intention: build AI that respects human agency, oversight and wellbeing. This paper argues that intention is not sufficient. AI systems are always deployed inside social conditions — professional hierarchies, institutional rules, time pressure, bias and conflicting stakeholder expectations — that shape whether human oversight is actually exercised. We introduce Socially Embedded Human-Centered AI as an extension of the Human Filter, the ethical mechanism through which humans interpret, question and evaluate AI outputs before they become consequential decisions. We map the three Human Filter functions — ethical sensitivity, value alignment and reflective elevation — against five embeddedness conditions, illustrate the framework across healthcare, law and business, and derive design implications for trustworthy, human-centered AI systems that remain accountable under real social pressure.
Human-Centered AI, Human Filter, Socially Embedded AI, Trustworthy AI, Human-AI Collaboration.
Olusola Bamidele Ayoade, Mumini Oyetunji Raji, Aminat Adejoke Akindele, Kemi Jemilat Yusuf-Mashopa, Muinat Folake Abdulrauff, Ibrahim Adebayo Raji, Fatima Bolanle Musah, Abiodun Timothy Adegbiji, Oluwafemi Micheal Amuda, Emmanuel Alayande University of Education, Oyo, Nigeria
Maize leaf diseases remain a major constraint to agricultural productivity, reducing crop yield and quality while threatening food security and farmers' income. Although Support Vector Machine (SVM) techniques have been widely applied for automated disease diagnosis, their effectiveness is often limited by inadequate optimisation, resulting in reduced classification accuracy and poor generalisation. This study proposes an Enhanced Hybrid Intelligent Machine Learning Model based on an Optimised Support Vector Machine (EHIMLM-SVM) that combines Enhanced Binary Particle Swarm Optimisation (EBPSO) with the Enhanced Reptile Search Algorithm (ERSA). The model was evaluated using 2,222 maize leaf images obtained from the Kaggle PlantVillage dataset. Preprocessing involved grayscale conversion, contrast enhancement, adaptive median filtering, Sobel edge detection, and extraction of colour, texture, and shape features. Experimental results produced 97.50% accuracy, 98.15% precision, 97.76% sensitivity, 97.80% specificity, and a 2.20% false positive rate, indicating reliable performance for precision agriculture and sustainable crop management.
EBPSO-ERSA, Hybrid Optimisation Algorithm, Maize Leaf Disease Classification, Precision Agriculture, Support Vector Machine (SVM).