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.
Georges Boyoko Wandjoli1, Yogo Modiambongo Felicite2, Prosper-Joël Bakoli Bondjemba1, 1Higher Institute of Rural Development of Mbandaka, Democratic Republic of the Congo, 2Université de Lisala
The integration of artificial intelligence into higher education is gradually transforming student feedback practices and learning strategies. Algorithmic assistance systems offer fast, personalized and accessible help. However, their effectiveness compared with traditional teacher feedback remains debated. This study compares the academic performance and perceptions of students who benefited from algorithmic assistance or traditional pedagogical feedback. A comparative quantitative approach was conducted with 120 students from the University of Lisala, divided into two groups of 60 participants. One group used Geonasoft Learning for three months, while the other received pedagogical feedback from teachers. The results show a slightly higher mean score for algorithmic assistance, but no statistically significant difference. The study concludes that algorithmic assistance can complement, but not replace, teacher feedback.
Artificial intelligence, algorithmic assistance, pedagogical feedback, academic performance, higher education.
Paul Kinion, Oregon State University, USA
This paper provides the second known solution to da Coi’s quartic, a problem posed and partially solved in Girolamo Cardano’s Ars Magna. It attempts to explain why half a millennium passed before the first solution was published.
AThe Scrubby Plant, Ruffini’s Algorithm, Ars Magna, Girolamo Cardano, Lodovico Ferrari .