Studying 5.6 million biomedical science articles published over three decades, we reconcile conflicts in a longstanding interdisciplinary literature on scientists' life-cycle productivity by controlling for selective attrition and distinguishing between research quantity and quality. While research quality declines monotonically over the career, this decline is easily overlooked because higher "ability" authors have longer publishing careers. Our results have implications for broader questions of human capital accumulation over the career and federal research policies that shift funding to early-career researchers - while funding researchers at their most creative, these policies must be undertaken carefully because young researchers are less "able" on average.

Emotions play a central role in shaping learning within digital environments. Although their effects may depend on how students' emotional experiences manifest into concrete behaviors, the links between these dimensions remain underexplored. This study investigates the most common behaviors during episodes of boredom, confusion, frustration, and engaged concentration in an educational game, as well as associations with situational interest, self-efficacy, prior knowledge, and learning gains, using interaction logs and sensor-free affect detectors. Results show that boredom is linked to off-task roaming, both consistently associated with lower motivation and learning. In contrast, behaviors during engaged concentration, frustration, and especially confusion vary widely, shaped by motivational traits and prior knowledge and offering diverse associations with learning. Concrete regulatory responses in these states—such as systematizing findings with in-game tools, skimming domain content to resolve doubts, or testing hypotheses—are positively associated with learning and motivation, reflecting students' ability to regulate emotions and address cognitive challenges. However, less constructive responses, such as aimless wandering, were tied to lower knowledge and motivation, underscoring the need for additional support. These findings extend existing affective theory by underscoring the importance of considering the behavioral dimension when analyzing students' emotions in digital learning environments.

Despite increased attempts to express equality in speech, biases often leak out through subtle linguistic cues. For example, the subject–complement statement (SCS, “Girls are as good as boys at math”) is used to advocate for equality but often reinforces gender stereotypes (boys are the standard against which girls are judged). We ask whether stereotypes conveyed by SCS can be counteracted by gesture. Two preregistered studies with 8- to 11-y-old children ( N = 320 total) investigate whether an equal gesture—two palms placed at the same height—mitigates the gender stereotype induced by SCS. Children who saw the equal gesture along with SCS were more likely to express egalitarian beliefs than children who saw no gesture or an unequal gesture. Children can extract meaning from gesture when making stereotypical inferences, suggesting that the equal gesture may prove to be an innovative, and simple, intervention to counteract stereotypes introduced by subtle language.

Automated essay scoring (AES) is to estimate the scores of essays automatically. Two types of AES models are commonly used: handcrafted feature-based and neural-based models. In this paper, we introduce AES systems based on the two types for evaluating the logical consistency of Japanese essays. In addition, to enhance the performance of models, we integrate the neural-based model with the handcrafted features: a hybrid AES system. In the experiment, we show the effectiveness of our hybrid AES system. Besides, most of our AES models obtained higher QWK scores than human evaluators. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Pedagogical agents offer significant promise for engaging students in learning. In this paper, we investigate students' conversational interactions with a pedagogical agent in a game-based learning environment for middle school science education. We utilize word embeddings of student-agent conversations along with features distilled from students' in-game actions to induce predictive models of student engagement. An evaluation of the models' accuracy and early prediction performance indicates that features derived from students' conversations with the pedagogical agent yield the highest accuracy for predicting student engagement. Results also show that combining student problem-solving features and conversation features yields higher performance than a problem solving-only feature set. Overall, the findings suggest that student-agent conversations can greatly enhance student models for game-based learning environments.

Across the past decade, open science has increased in momentum, making research more openly available and reproducible. Artificial Intelligence (AI), especially within education, has produced effective models to better predict student outcomes, generate content, and provide a greater number of observable features for teachers. While completed, generalized AI models take advantage of available open science practices, models used during the actual research process are not made available. In this tutorial, we will provide an overview of open science practices and their benefits and mitigation within AI education research. In the second part of this tutorial, we will use the Open Science Framework to make, collaborate, and share projects - demonstrating how to make materials, code, and data open. The final part of this tutorial will go over some mitigation strategies when releasing datasets and materials so other researchers may easily reproduce them. Participants in this tutorial will learn what the practices of open science are, how to use them in their own research, and how to use the Open Science Framework. The website (https://aied2023-tutorial.howtoopenscience.com/ ) and associated resources can be found on an Open Science Framework project (https://doi.org/10.17605/osf.io/yd9kr ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Student performance prediction continues to be a focus of research in educational data mining due to its many potential benefits. While teachers’ assessment reports are a crucial part of the educational process, they have not been commonly used in performance prediction. We propose a model that uses similarity learning as an embedding-enhancing technique. Results outperform earlier research with an average accuracy of 73% for detecting strong performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evidence-based learning strategies, such as the testing effect, might help address the achievement gap. However, exploiting the testing effect depends on having a set of instructional activities with fine-grained tagging. While instructors might find questions in textbooks, they often lack fine-grained tagging, and data labeling is laborious. Despite much research on text classification, to our best knowledge, state-of-the-art question classifiers are mostly based on extensive models (i.e., BERT) and English text. Respectively, those are incompatible with the resource-constrained devices (e.g., mobile) and languages (e.g., Portuguese) of many underprivileged countries in the global south. Therefore, we developed a question classifier on top of DistilBERT, a version of BERT compatible with resource-constrained applications, using grid search and hold-out. Based on a corpus of 1045 coding questions written in Brazilian Portuguese, we found a model that achieved a near-perfect performance on unseen data, similar to last-generation results using BERT for English text. Thus, we present a step towards equitable education by i) providing underprivileged Portuguese-speaking countries with the support that enables opportunities already available for first-world countries and ii) demonstrating the feasibility of creating resource-constrained applications compatible with state-of-the-art AIED systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Educational philosophies have slowly focused on differentiated and self-learning systems that respectively emphasize tailoring instruction to meet individual needs and gathering, processing, and retaining knowledge without the help of another person in recent years. In this regard, creating opportunities to further self-learning resources has become increasingly important. Some people started to prefer these over traditional learning practices for various reasons, such as the difficulty of transportation in metropolises or fitting their timetables to in-person lessons. The creation of such platforms or opportunities for physical education, however, proves to be more difficult as individuals require continuous and precise feedback regarding the usage of their bodies. Accordingly, we have developed an augmented reality application that presents a platform for dance that focuses on differentiated and self-learning principles with accurate feedback. We built the augmented reality (AR) app prototype using the Swift programming language and used the MoveNet pose detection model along with our own neural network to capture the body position. Our proposal could prove a valuable addition to learning physical activities assisted by AI systems since the application of AI technologies to dance and physical education could be improved by further investigation and research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Artificial Intelligence in Education (AIED) is a driving force to improve education. Nevertheless, policymakers from the Global South fear that AI will increase the digital divide and reduce the opportunities for students in these regions to thrive. To address this problem, we analyzed the past 30 years of data on four aspects of the digital divide. Then, based on these findings and a series of discussions with stakeholders (e.g., policymakers), we proposed the concept of AIED Unplugged. An approach to creating AI-based educational technologies that do not require changes in current school settings (e.g., infrastructure), do not rely on stable internet access, and do not ask for digital skills to use them. We applied this concept to redesign an education policy in Brazil to help students improve their writing skills. Our results show a reduction in time, cost and complexity to running the policy, and a positive impact on more than 500,000 students in 7,000 schools in the country. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.