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In the so-called “post-truth” world, there exists widespread confusion and disagreement over what is known, how to know, and who to trust. Current education has largely failed to meet the challenges of this world. Grounded in a new analysis of the goals of epistemic education, we argue for new directions in instruction. Our analysis specifies three components of epistemic cognition that education should address: epistemic aims, ideals, and reliable processes. Apt epistemic performance of these components has five interwoven aspects: cognitive engagement in epistemic performance, adapting performance to diverse contexts, metacognitive regulation and understanding of performance, caring and enjoyment, and participation in performance with others. Using this framework, we show how three emblematic “post-truth” problems stem from specific breakdowns in these five aspects. We then use this analysis to argue for new directions in curriculum, instruction, and research that are needed to promote successful epistemic performance in the “post-truth” landscape.

Building on the insights of the first two publications in the Strengthening the Impact of Education Research series, this third and final report shifts the focus from policy makers to the field, exploring the pivotal role of knowledge intermediaries. These organisations facilitate engagement with research among policy makers and practitioners, supporting evidence-informed decision making and practices in education. Drawing on new data from 288 such organisations across 34 countries, the report delves into the ways these intermediaries bridge the gap between education research, policy and practice. It highlights their efforts to disseminate evidence, foster collaboration, and build capacity for research use, while also identifying gaps in skills and infrastructure that demonstrate the need for further professionalisation of knowledge mobilisation. By offering actionable insights for policy makers, practitioners, researchers and intermediaries, this report emphasises the importance of co-ordination, evaluative thinking, and system-level strategies to maximise the impact of education research. It serves as a valuable resource for those seeking to understand and enhance the landscape of knowledge mobilisation, ensuring that education systems are better equipped to integrate evidence into policy and practice.

Recently, the concept of evaluative judgement has gained attention as a pedagogical approach to classroom formative assessment practices. Evaluative judgement is the capacity to be able to judge the work of oneself and that of others, which implies developing knowledge about one’s own assessment capability. A focus on evaluative judgement helps us to better understand what is the influence of assessment practices in the regulation of learning. In this paper, we link evaluative judgement to two self-regulated learning models (Zimmerman and Winne) and present a model on the effects on co-regulation of learning. The models help us to understand how students can be self-regulated through developing their evaluative judgement. The coregulation model visualises how the learner can become more strategic in this process through teacher and peer assessment in which assessment knowledge and regulation strategies are shared with the learner. The connections we make here are crucial to strengthening our understanding of the influence of assessment practices on students’ learning.

Evaluative judgement is the capability to make decisions about the quality of work of oneself and others. In this paper, we propose that developing students' evaluative judgement should be a goal of higher education, to enable students to improve their work and to meet their future learning needs: a necessary capability of graduates. We explore evaluative judgement within a discourse of pedagogy rather than primarily within an assessment discourse, as a way of encompassing and integrating a range of pedagogical practices. We trace the origins and development of the term 'evaluative judgement' to form a concise definition then recommend refinements to existing higher education practices of self-assessment, peer assessment, feedback, rubrics, and use of exemplars to contribute to the development of evaluative judgement. Considering pedagogical practices in light of evaluative judgement may lead to fruitful methods of engendering the skills learners require both within and beyond higher education settings. (HRK / Abstract übernommen).

What is the point of ideology critique? Prominent Anglo-American philosophers recently proposed novel arguments for the view that ideology critique is moral critique, and ideologies are flawed insofar as they contribute to injustice or oppression. We criticize that view and make the case for an alternative and more empirically oriented approach, grounded in epistemic rather than moral commitments. We make two related claims: (a) ideology critique can debunk beliefs and practices by uncovering how, empirically, they are produced by self-justifying power and (b) the self-justification of power should be understood as an epistemic rather than moral flaw. Drawing on the recent realist revival in political theory, we argue that this genealogical approach has more radical potential, despite being more parsimonious than morality-based approaches. We demonstrate the relative advantages of our view by discussing the results of empirical studies on the contemporary phenomenon of neopatriarchy in the Middle East and North Africa.

Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations between academic self-efficacy, academic stress, performance expectations, and AI dependency. It also identifies the negative consequences of AI dependency. Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations. The top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The findings provide explanations and solutions to mitigate the negative effects of AI dependency.

How can students’ competence be developed through peer assessment? This paper focuses on how relevant variables such as participation, evaluative judgement and the quality of the assessment interact and influence peer assessment. From an analysis of 4 years of data from undergraduate classes in project management, it develops a model of causal relationships validated using the PLS-SEM method. It demonstrates relationships between these variables and considerers the influence of students’ competence and the mediating nature of feedback and self-regulation on the process. It points to how peer assessment practices can be improved whilst highlighting how evaluative judgement and feedback are two key elements that can be addressed to deliver the effective development of students’ competence.

Research continues to accumulate showing that in instructor evaluations students are biased against women. This article extends these analyses by examining the dynamics between evaluations and gender and race/ethnicity. In a quasi-experimental design, faculty members teaching identical online courses recorded welcome videos that were presented to students at the course onset, constituting the sole exposure to perceived gender and race/ethnicity. This enables exploration of whether and to what degree the instructors’ characteristics influenced student evaluations, even after holding all other course factors constant. Findings show that instructors who are female and persons of color receive lower scores on ordinal student evaluations than those who are white males. Overall, we add further evidence to a growing literature calling for student evaluations of teaching (SETs) reform and extend it to encompass the effects on racial/ethnic minorities in addition to women.

This study aimed to examine an assumption regarding whether generative artificial intelligence (GAI) tools can overcome the cognitive intensity that humans suffer when solving problems. We examine the performance of ChatGPT and GPT-4 on NAEP science assessments and compare their performance to students by cognitive demands of the items. Fifty-four 2019 NAEP science assessment tasks were coded by content experts using a two-dimensional cognitive load framework, including task cognitive complexity and dimensionality. ChatGPT and GPT-4 answered the questions individually and were scored using the scoring keys provided by NAEP. The analysis of the available data for this study was based on the average student ability scores for students who answered each item correctly and the percentage of students who responded to individual items. The results showed that both ChatGPT and GPT-4 consistently outperformed most students who answered each individual item in the NAEP science assessments. As the cognitive demand for NAEP science assessments increases, statistically higher average student ability scores are required to correctly address the questions. This pattern was observed for Grades 4, 8, and 12 students respectively. However, ChatGPT and GPT-4 were not statistically sensitive to the increase of cognitive demands of the tasks, except for Grade 4. As the first study focusing on comparing cutting-edge GAI and K-12 students in problem-solving in science, this finding implies the need for changes to educational objectives to prepare students with competence to work with GAI tools such as ChatGPT and GPT-4 in the future. Education ought to emphasize the cultivation of advanced cognitive skills rather than depending solely on tasks that demand cognitive intensity. This approach would foster critical thinking, analytical skills, and the application of knowledge in novel contexts among students. Furthermore, the findings suggest that researchers should innovate assessment practices by moving away from cognitive intensity tasks toward creativity and analytical skills to more efficiently avoid the negative effects of GAI on testing.