Steffen Bohni Nielsen – författare

Artificial Intelligence and Evaluation: Emerging Technologies and Their Implications for Evaluation is a groundbreaking exploration of how the landscape of program evaluation will be redefined by artificial intelligence and other emerging digital technologies.In an era where digital technologies and artificial intelligence (AI) are rapidly evolving, this book presents a pivotal resource for evaluators navigating the transformative intersection of their practice and cutting-edge technology. Addressing the dual dimensions of how evaluations are conducted and what is evaluated, a roster of distinguished contributors illuminate the impact of AI on program evaluation methodologies. Offering a discerning overview of various digital technologies, their promises and perils, they carefully dissect the implications for evaluative processes and debate how evaluators must be equipped with the requisite skills to harness the full potential of AI tools. Further, the book includes a number of compelling use cases, demonstrating the tangible applications of AI in diverse evaluation scenarios. The use cases range from the application of GIS data to advanced text analytics. As such, this book provides evaluators with inspirational cases on how to apply AI in their practice as well as what pitfalls one must look out for.Artificial Intelligence and Evaluation is an indispensable guide for evaluators seeking to not only adapt to but thrive in the dynamic landscape of evaluation practices reshaped by the advent of artificial intelligence.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Artificial Intelligence and Evaluation: Emerging Technologies and Their Implications for Evaluation is a groundbreaking exploration of how the landscape of program evaluation will be redefined by artificial intelligence and other emerging digital technologies.In an era where digital technologies and artificial intelligence (AI) are rapidly evolving, this book presents a pivotal resource for evaluators navigating the transformative intersection of their practice and cutting-edge technology. Addressing the dual dimensions of how evaluations are conducted and what is evaluated, a roster of distinguished contributors illuminate the impact of AI on program evaluation methodologies. Offering a discerning overview of various digital technologies, their promises and perils, they carefully dissect the implications for evaluative processes and debate how evaluators must be equipped with the requisite skills to harness the full potential of AI tools. Further, the book includes a number of compelling use cases, demonstrating the tangible applications of AI in diverse evaluation scenarios. The use cases range from the application of GIS data to advanced text analytics. As such, this book provides evaluators with inspirational cases on how to apply AI in their practice as well as what pitfalls one must look out for.Artificial Intelligence and Evaluation is an indispensable guide for evaluators seeking to not only adapt to but thrive in the dynamic landscape of evaluation practices reshaped by the advent of artificial intelligence.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Success in Evaluation takes a fundamentally different approach to the mainstream supply side discussion of evaluation quality, utilization, and learning. The contributors believe that a systematic focus on success will lead to increased awareness of evaluation and its findings, a more positive attitude, and a greater chance of actual evaluation use.This book offers many different lessons on how to improve evaluation design, research processes, and reporting. It is a realistic look at performance management, the evidence movement, and the demand barriers that so often block the role evaluators can play in organizational learning and decision-making.International case studies and lessons are included that both explain success-oriented methods and share insightful lessons from the real world. Together, they present a convincing case that evaluation for success allows for increased constructive interaction amongst both stakeholders and evaluators and, as a result, learning processes and outcomes will improve.

From Algorithms to Evidence: Using GenAI in Evaluation Practice offers a timely, practice-grounded guide for evaluators and development professionals navigating the fast-moving world of generative AI. Building on the foundations laid in Artificial Intelligence and Evaluation (2025), this volume moves decisively from theory to application. It documents how evaluators across the globe are already using GenAI in real projects, showing not only what worked but also what failed, why, and under what conditions. The result is a clear, practitioner-centered resource that cuts through hype and provides grounded, real-world insight.Structured around the seven phases of the evaluation lifecycle (design, structuring and inception, data collection, data analysis, reporting, judgment, and utilization), this book offers concrete examples of how GenAI is being integrated into everyday evaluation tasks. Contributors explain their rationale for using AI tools, the steps they took, and the results they achieved. Crosscutting chapters synthesize lessons on methodological adaptation, evolving evaluator competencies, and the ethical and professional standards needed to use GenAI responsibly. Throughout, this volume emphasizes ‘hybrid intelligence,’ showing how human expertise and AI-enabled methods can work together to strengthen evaluative reasoning.Clear, accessible, and grounded in real practice, From Algorithms to Evidence fills a critical gap in the literature. It provides evaluators, policymakers, and organizational leaders with practical guidance for building more adaptive, data-informed, and future-ready evaluation systems while keeping equity, transparency, and human judgment at the center of their work.

Success in Evaluation takes a fundamentally different approach to the mainstream supply side discussion of evaluation quality, utilization, and learning. The contributors believe that a systematic focus on success will lead to increased awareness of evaluation and its findings, a more positive attitude, and a greater chance of actual evaluation use. This book offers many different lessons on how to improve evaluation design, research processes, and reporting. It is a realistic look at performance management, the evidence movement, and the demand barriers that so often block the role evaluators can play in organizational learning and decision-making. International case studies and lessons are included that both explain success-oriented methods and share insightful lessons from the real world. Together, they present a convincing case that evaluation for success allows for increased constructive interaction amongst both stakeholders and evaluators and, as a result, learning processes and outcomes will improve.

Success in Evaluation takes a fundamentally different approach to the mainstream supply side discussion of evaluation quality, utilization, and learning. The contributors believe that a systematic focus on success will lead to increased awareness of evaluation and its findings, a more positive attitude, and a greater chance of actual evaluation use. This book offers many different lessons on how to improve evaluation design, research processes, and reporting. It is a realistic look at performance management, the evidence movement, and the demand barriers that so often block the role evaluators can play in organizational learning and decision-making. International case studies and lessons are included that both explain success-oriented methods and share insightful lessons from the real world. Together, they present a convincing case that evaluation for success allows for increased constructive interaction amongst both stakeholders and evaluators and, as a result, learning processes and outcomes will improve.