assessment-design
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- PaperAssessment & Evaluation in Higher Education17 Jul 2026
Navigating the moral panic: encouraging appropriate use of GenAI in the classroom rather than condemning innovation as disruption
Jennifer M. Krebsbach, Victoria L. Cross
A study of eight iterations of a Data Visualisation course compared student performance on knowledge and application quizzes across three conditions: pre-GenAI, GenAI-available, and GenAI-integrated. When GenAI was available but not taught, applied quiz performance decreased and variability increased. When GenAI was embedded into instruction, applied performance and variability returned to pre-GenAI levels, and knowledge quiz variability decreased, suggesting greater equity.
Original abstract
Faculty are divided on how to respond to generative artificial intelligence (GenAI). Many respond in similar ways to how faculty responded to other disruptive technologies (e.g. calculators, word processors), fearing that students will cheat more and learn less. Rather than fear GenAI, we view it as the next disruptive technology that will become normalised in the classroom and examine how intentionally integrating it into instruction increases both academic integrity and authenticity. Using data from eight iterations of a lower-division Data Visualisation course, we compare student performance on two quiz types (knowledge and application) in three conditions (pre-GenAI, GenAI-available, and GenAI-integrated). In the GenAI-integrated condition, GenAI use was taught and encouraged. During the GenAI-available quarters, performance on the applied questions decreased and variability increased, indicating that only some students were using GenAI, and ineffectively. When GenAI was embedded into the assessments, applied performance and variability returned to pre-GenAI levels. Knowledge questions fell below baseline with the new format, but so did variability, potentially indicating greater equity across the quiz. By teaching students to use GenAI, we aim to support long-term learning, improve accountability, ensure authenticity, and support instructors in addressing concerns about potentially problematic technology in the classroom.
- PaperETS Research Report Series10 Jul 2026
Identifying High-Leverage Practices for Guiding the Development of Teaching Assessments
Geoffrey Phelps, Heather Howell, Jamie Mikeska, Caroline Wylie
Identifies eight high-leverage practices (HLPs) for teaching to guide the development of a next generation of teaching assessments. The HLPs are selected from an existing framework of 34 practices with empirical and theoretical backing. The report describes the selection methods and provides initial recommendations for appropriate assessment methods for each HLP.
Original abstract
This report identifies eight high-leverage practices (HLPs) for teaching that are intended for use in guiding the development of a next generation of teaching assessments. The identified HLPs are drawn from an existing framework that provides empirical and theoretical research backing for 34 HLPs that make up the work of teaching. The report describes the methods used to select a subset of eight HLPs and provides initial recommendations for the assessment methods that are most appropriate for each of these eight HLPs.
- PaperAssessment & Evaluation in Higher Education4 Jul 2026
From authentic products to authenticated processes: a systematic conceptual review of authentic assessment in AI-rich higher education
Vangelis Tsiligiris
This systematic conceptual review synthesizes 37 sources to develop a six-dimension framework for authentic assessment in higher education under generative AI. The framework distinguishes authentic products from authenticated processes, arguing that validity depends on architectures that make human judgement and verification visible. The review offers practical design questions for program-level redesign.
Original abstract
This article presents a systematic conceptual review of authentic assessment in higher education and develops a six-dimension framework for assessment design in digitally mediated and AI-rich conditions. Drawing on a retained corpus of 37 substantive sources, it synthesises foundational and contemporary literature on task fidelity, evaluative judgement, process evidence, inclusion, and AI-mediated validity. The synthesis shows that authentic assessment should not be reduced to workplace simulation or treated primarily as a response to academic misconduct. It is better understood as a multidimensional design orientation spanning contextual fidelity and consequential relevance, cognitive demand and evaluative judgement, process transparency and integrity, student agency and bounded choice, inclusivity and representational fairness, and AI-aware validity and ethical practice. The article’s main contribution is to distinguish authentic products from authenticated processes. It argues that assessment validity under generative AI depends not only on realistic outputs, but on architectures that make human judgement, verification, and responsibility visible. The framework offers review questions that support module-level and programme-level redesign by linking authenticity, evidence, validity, and accountable student judgement.
- PaperAssessment & Evaluation in Higher Education3 Jul 2026
AI-resistant and AI-resilient assessment in higher education: a systematic review of validity-grounded strategies, institutional frameworks, and equity implications
Asrat Genet Amnie
This systematic review distinguishes AI-resistant (preventing AI access) from AI-resilient (assessing genuine human cognitive performance) assessment in higher education. It proposes a Six-Layer AI-Resilient Assessment Stack grounded in validity logic and identifies six evidence-based assessment categories. The review finds AI-detection tools insufficient as primary responses and highlights challenges including equity, faculty development, and data protection.
Original abstract
The emergence of large language models has precipitated a fundamental disruption to higher education assessment. Conventional instruments are susceptible to AI-assisted completion, threatening construct validity across disciplines and institutional contexts. When submitted work reflects AI capability rather than student competency, the inferential chain from performance to qualification is invalidated – a problem of design rather than detection. This systematic review makes two contributions. First, it theorises and grounds the distinction between AI-resistant assessment, which seeks to prevent or impede AI access through containment, and AI-resilient assessment, which assesses genuine human cognitive performance of intrinsic educational value regardless of the AI tools that exist. Second, it synthesises peer-reviewed empirical literature, policy documentation, and sector guidance published between 2022 and 2025. A PRISMA 2020-compliant search of five databases was conducted; two independent reviewers screened all records with substantial inter-rater agreement. Forty-seven peer-reviewed studies, 23 policy documents, and 11 sector reports were included. Six evidence-based assessment categories were identified, taxonomised, and evaluated against a structured adversarial threat taxonomy. AI-detection tools are structurally insufficient as primary responses. A Six-Layer AI-Resilient Assessment Stack, grounded in multi-trait multi-method validation logic, is proposed as an integrated institutional framework. Persistent challenges include equity, reliability, faculty development, and data protection.
- PaperAssessment & Evaluation in Higher Education15 Jun 2026
Project-based assessment: a scoping review of concepts, implementations, and future directions
Meichun Huang, Lin Xiaohong, Baichang Zhong
This scoping review synthesizes 75 articles on Project-Based Assessment (PBA), revealing it as a constructivist, learning-centered assessment paradigm that integrates multidimensional functions. Implementation is common in higher education and applied disciplines, targeting higher-order thinking, but faces challenges like heavy burdens and reliability concerns. Future directions include reconstructing evidence frameworks and redefining teacher roles.
Original abstract
Project-Based Assessment (PBA) is an integrated assessment paradigm widely applied in STEM and other interdisciplinary fields. It holds significant potential for fostering students’ higher-order thinking skills and enhancing their comprehensive abilities. However, research in this field remains fragmented and lacks a systematic synthesis. This study addresses this gap by conducting a scoping review of 75 articles aiming to elucidate core concepts, diagnose implementation practices, and provide strategic guidance for future development. The synthesis revealed that PBA is a comprehensive assessment paradigm grounded in constructivism, which integrates multidimensional assessment functions based on authentic problems and process-oriented assessment. Essentially, PBA is positioned as a learning-centered assessment. The findings indicated that PBA is predominantly implemented in higher education and applied disciplines, primarily targeting higher-order cognitive objectives through mixed-method assessment tools. While PBA demonstrates a positive trend in fostering learner engagement, its effectiveness is often hindered by heavy implementation burdens and concerns regarding assessment reliability. Furthermore, several critical deficiencies were identified, including a teacher-centric power structure, the absence of initial assessment phases, and insufficient iterative improvement. Consequently, to enable the effective implementation of PBA, future efforts should focus on reconstructing evidence frameworks, redefining teacher roles, and refining practice models.
- PaperAssessment & Evaluation in Higher Education8 Jun 2026
Authentic assessment in higher education: conceptual evolution, key debates and implications for practice
Yusuf Josiah
Authentic assessment in higher education has evolved from early performance-oriented tasks to contemporary models emphasizing sustainability, digital mediation, and lifelong learning. The article traces this conceptual evolution, highlights debates around employability, equity, and academic integrity, and provides a framework for future research on implementation across diverse contexts.
Original abstract
Authentic assessment has become increasingly central in higher education, reflecting a shift away from traditional, decontextualised testing toward assessment practices emphasising meaningful learning, integrated competence and the application of knowledge in context. Despite its growing prominence, authentic assessment remains conceptually fragmented, encompassing task-focused, competence-oriented, and more recent embedded and future-oriented interpretations. This article presents a comprehensive conceptual review tracing the evolution of authentic assessment from its early performance-oriented formulations in the late 1980s to contemporary conceptualisations emphasising sustainability, digital mediation, ethical engagement with emerging technologies and lifelong learning capabilities. The article synthesises historical and theoretical developments to examine how authenticity has been reframed in response to changing educational priorities and institutional contexts. It also highlights key debates in authentic assessment and tensions surrounding employability, equity, standardisation, digitally mediated learning environments and academic integrity. By providing an analytically structured account of the conceptual evolution of authentic assessment, the article clarifies conceptual ambiguities, situates contemporary developments within a broader historical trajectory and provides a foundation for future empirical research on the mechanisms, implementation and outcomes of authentic assessment across diverse higher education contexts.
- PaperAssessment & Evaluation in Higher Education4 Jun 2026
When ‘good teaching’ isn’t enough: learning environments that affect student feedback literacy
Caroline Xin Liu, Lily M. Zeng
Assessment for understanding and clear goals significantly predict student feedback literacy, while good teaching and teacher feedback do not, according to a mixed-methods study of Mainland Chinese undergraduates in Hong Kong. The findings emphasize that constructive alignment in programme-level learning environments is critical for cultivating feedback literacy and improving learning outcomes.
Original abstract
Despite the growing interest in student feedback literacy for student learning, how it is shaped within programme-level learning environments and its influence on learning outcomes remain underexplored. Even fewer studies have focused on how non-local students may experience this despite the key factors that were reported to have an impact on student feedback literacy would be different in their case. Using mixed methods, this project investigates the relationship among programme-level learning environments, feedback literacy, and learning outcomes. Study 1 analysed quantitative data from 547 Mainland Chinese undergraduates in Hong Kong. Structural equation modelling revealed that assessment for understanding and clear goals and standards significantly predicted feedback literacy, which mediated the effects of learning environments on learning outcomes, whereas good teaching and teacher feedback were not significantly associated with student feedback literacy. Study 2, based on interviews with fifteen students, indicated that aligned assessment designs and transparent standards were the key factors that enhanced student feedback literacy, explaining why teaching-related aspects may be less directly involved in creating affordances. The findings advance understanding of feedback literacy as influenced by the learning environments in the higher education context. The study highlights the critical role of constructive alignment in cultivating sustainable feedback literacy and improving student learning outcomes.
- PaperETS Research Report Series21 Nov 2025
Preparing K–12 Students With AI Literacy: Proposed Framework, Progression, and Task Design Principles
Srijita Chakraburty, Teresa Ober, Lei Liu
Proposes a conceptual framework for AI literacy for K-12 students, including a learning progression and task design principles. Developed through evidence-centered design, the framework integrates foundational knowledge, ethical awareness, and practical AI applications. The progression maps skill development and offers educators scaffolded instruction strategies, while three design principles guide creation of relevant, accessible, and skill-advancing activities.
Original abstract
This paper presents a conceptual framework for AI literacy, a hypothesized learning progression, and assessment design principles for advancing AI literacy among K–12 learners. Recognizing the importance of technical competencies alongside ethical awareness, the framework integrates foundational knowledge, societal implications, and practical applications of AI. Key competencies include ethical decision-making, AI-powered collaboration, and critical evaluation of AI outputs. Developed through an evidence-centered design (ECD) process involving a review of existing literature and frameworks, the proposed AI literacy framework and progression maps a hypothesized trajectory of students’ skill development, providing a structured pathway for improvement with behavior indicators connected to core AI literacy subskills. In this way, the framework and progression may offer educators a roadmap to apply scaffolded and differentiated teaching strategies that actively foster learners’ skill acquisition. To further support connections between assessment and instruction, we introduce three design principles for task design: ensuring relevance to learners, minimizing barriers to resource access, and providing opportunities for skill advancement. These design principles may guide the creation of activities that evaluate and enhance students’ AI literacy. By aligning scaffolded assessments and learning activities with the progression, this framework bridges instruction, assessment, and students’ skill development. It ultimately may be used to support students in developing skills to critically and ethically engage with AI technologies, preparing them to navigate the digital landscape by fostering inclusive instruction that deepens students’ understanding of AI concepts. Chakraburty, S., Ober, T. M., & Liu, L. (2025). Preparing K–12 students with AI literacy: Proposed framework, progression, and task design principles (Research Report No. RR-25-14). ETS. https://doi.org/10.64634/46jn1p41