Learning Tool Research Articles

Generative AI

The integration of Generative AI (GenAI) technology in higher education demands active and creative pedagogical responses. In this paper we report on an emerging creative educational design approach which introduces activities for working with GenAI in imaginative ways. Through multidisciplinary research, we are iteratively designing activities to engage educators and students in creative and critical inquiry of GenAI with metaphors. First, we introduce the background and rationale for using metaphor to explore GenAI in education. Our approach to generative AI draws on postdigital ideas that consider technology as entwined in our lives, rather than as separate tools. Put simply, GenAI is neither inherently good nor bad for education. Research on metaphor as a powerful learning tool of analogy is reviewed. A detailed account of workshop learning activities is then provided, including their design, implementation, and preliminary findings. The aim is to broaden approaches to using and integrating such technologies, and to serve as an alternative model for other educators to adapt for their own practices. Finally, we discuss future, creative possibilities with GenAI and metaphor.

Exploring the Levels of eLearning Interactivity: A Review of Research Literature

eLearning has become a widespread and integral part of modern education and training. A key aspect of effective eLearning is interactivity, which can enhance engagement, learning, and knowledge retention. This paper provides a comprehensive review of the research literature on the levels of interactivity in eLearning environments. The review examines different taxonomies and frameworks proposed to categorize and assess the various levels of interactivity, from low-level interactions such as clicking and scrolling, to more advanced interactive features like simulations, virtual reality, and collaborative learning tools. The paper also discusses the pedagogical benefits of increasing interactivity, as well as the design and implementation considerations for achieving optimal levels of interactivity in eLearning. Finally, the review identifies gaps in the current research and suggests future directions for studying the impact of interactivity on eLearning outcomes.

INTEGRATING INTERTEXTUAL AND COMMUNITY RESPONSE ANALYTICAL LENSES TO TURN SYAIR CENDAWAN PUTIH’S A CHILDREN'S STORY “THE ADVENTURE OF CENDAWAN PUTIH” INTO A SCIENCE LEARNING

This study explores the transformation of the Cendawan Putih poem into prose suitable for children's science learning, examining the adaptation process and public perception. Using a qualitative approach, the study employs intertextual theory to analyze textual transformations such as expansion, conversion, modification, and citation and community response theory to capture stakeholders’ views. The research investigates how the Cendawan Putih poem has been creatively adapted into the children’s story The Adventure of Cendawan Putih to enhance science learning while maintaining cultural and educational value. The findings reveal that adapting Cendawan Putih for children’s literature not only preserves core narrative elements but also integrates science concepts relevant to young readers. The adaptation utilizes techniques such as expansion and modification, enriching the original storyline to highlight nature, ecosystems, and environmental science in an accessible format for children. Public feedback, collected through Focus Group Discussions (FGDs) with cultural figures, educators, academics, and youth, shows broad support for this transformation. Stakeholders emphasize the story’s potential to foster cultural awareness and introduce foundational science concepts, making it an innovative tool for learning. This study highlights how traditional literary forms can be reimagined for children’s science learning, merging cultural heritage with modern educational needs. By presenting Cendawan Putih in a format that supports both cultural preservation and science education, this work positions the story as an intergenerational bridge and a valuable addition to literature-based science learning. It underscores the importance of community involvement in literary adaptation, providing new insights for literature, cultural education, and science pedagogy.

Immersive Virtual Reality: A Novel Approach to Second Language Vocabulary Acquisition in K-12 Education

Today, immersive virtual reality (IVR) is increasing in popularity in a broad range of fields, including science, pedagogy, engineering and so forth. Therefore, this study discusses the Unified Theory of Acceptance and Use of Technology (UTAUT), which can be used to examine the factors that influence the adoption of immersive VR in the classroom, particularly in second language (L2) vocabulary acquisition. The sample for this study included 32 intermediate students and their teacher. For the purpose of evaluation, the researcher used partial least squares structural equation modelling (PLS-SEM) techniques to analyze the results. The findings of the students’ survey showed that performance expectancy, effort expectancy and social influence were seen to have had a positive impact on the intention to use immersive VR. Likewise, the findings indicated that facilitating conditions were seen to have had a positive impact on the use behavior of actually using immersive VR, whereas behavioral intentions did not. In addition, the teacher’s survey demonstrated a favorable view regarding the potential of immersive VR technology to support teaching L2 vocabulary acquisition. This particular study encouraged educators and educational technologists to utilize immersive VR games as a teaching–learning tool to reduce the challenge of L2 vocabulary acquisition.

The impact of virtual laboratories on the academic performance of grade 12 learners in impulse and momentum

Virtual laboratories (labs) have evolved as an important tool in the sciences, delivering interactive and engaging platforms for learners to conduct experiments and comprehend complex concepts without the limitations of physical resources. They are an emerging teaching tool that offers interactive and immersive experiences that improve comprehension of complicated scientific concepts. This study assesses the impact of virtual labs on the academic achievement of Grade 12 learners on the topic of impulse and momentum in physics. It uses a quasi-experimental methodology to compare the performance of learners who used virtual labs to those who learned in the prevailing theoretical settings. A pre-test and post-test were used to assess learners’ academic success, and surveys were used to gather learner feedback on the learning tool. Preliminary data analysis indicates a statistically significant improvement in the test scores of learners using virtual labs, corroborating earlier studies that highlight the advantages of technologically enhanced learning environments. Furthermore, qualitative feedback indicates that learners found virtual labs more interesting and accessible, resulting in a more conducive learning environment. This work adds to the expanding body of evidence supporting the use of virtual labs in science teaching, especially in resource-constrained contexts where traditional laboratory equipment is limited or non-existent. The implications for curriculum design and educational policy are examined, with a focus on the importance of investing in digital infrastructure to support innovative teaching approaches.

The Introduction of Automation in Control of Virtualized Laboratory Complex Components at Higher Educational Institution

The intensity of the use of remote work and learning tools has increased significantly during the pandemic and has led to the emergence of new tools and a significant increase in the capabilities of previously developed technology platforms. The introduc­tion of modern technologies based on software control and complex visualization for the creation of virtual laboratory complexes in educational institutions provides virtually unlimited opportunities for organizing individual and distance forms of practical classes. Modern learning management platforms that able to integrate with the database of university students makes it possible to evaluate the quality of students' work with various types of course learning materials in real time. The article shows that the use of virtual communication infrastructures in connection to the Moodle learning management platform will provide end-to-end control of the theoretical and practical components of the educational process and will automate the processes of preparing and conducting practical classes, which will improve the quality of the educational process and the teacher efficiency.

AI-powered language learning: The role of NLP in grammar, spelling, and pronunciation feedback

Abstract. The introduction of artificial intelligence (AI) and natural language processing (NLP) in language learning has transformed the practices of education. This paper analyzes the role of AI-powered tools in English language learning with specific focus on automated essay scoring (AES), speech recognition, and advanced grammar and spelling checkers. By examining the accuracy of the most effective AES systems, it is evident that automated feedback has increased the quality of writing by providing detailed, consistent, and objective feedback on submitted essays. Speech recognition is another tool that is being used to assess pronunciation and fluency of learners. The software is constantly improving, with advances in NLP and machine learning. The technology for fluency tests is becoming more accurate, and is helping to improve learner outcomes significantly. Lastly, we examine advanced grammar and spelling checkers, which are increasingly used for correcting the contextual errors of individual learners. The AI systems adapt to the user and their needs, which make the corrections more effective. This paper offers an overview of how AI is leading to personalized and effective language learning, ultimately enhancing the educational experience of students and embracing future possibilities in education technology.

Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update.

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/.

COVID-19 public health restrictions and opioid overdoes: a summative content analysis of emergency medical services records in three Texas counties

BackgroundWith the broad recognition of increased U.S. opioid overdose (OOD) rates between 2020 and 2021, media, public health, and healthcare organizations have raised significant concerns over the emergence of a simultaneous COVID-19-opioid “twindemic.” Research in this area has explored the possible relationships between negative externalities associated with the pandemic and/or COVID-19 public health interventions and increased risks for opioid use and overdose alongside diminished outcomes following OOD events.MethodsThe study offers a summative content analysis of Emergency Medical Service (EMS) responses to opioid overdose (OOD) events before and after the institution of COVID-19 restrictions. Specifically, the study investigates three Texas counties to evaluate changing OOD rates, patient demographics, and OOD event features. The analysis uses a previously validated machine learning tool to identify OOD events and conducted a summative content analysis of identified events.ResultsA total of 1170 OOD responses events were identified in the three-county dataset. This includes 874 in Travis County, 242 in El Paso County, and 54 in Williamson County. Each county experienced modest changes in EMS calls for OOD events between the pre-restriction and public health restriction time periods. Travis County’s OOD event rate declined from 454 to 420. El Paso’s increased from 103 to 139, and Williamson County’s increased from 23 to 31. These changes were not significant as percentage of possible OOD events or based on by-month comparison. The notable differences between pre-restriction and public health restriction periods were significant decreases in documentation of patient race/ethnicity in Travis and Williamson Counties, significant decreases in housing insecurity and use alone in Travis County, and an increase in transport refusal after treatment in the field in Travis County.ConclusionsUltimately the results presented here problematize prevailing analyses about the so-called opioid-COVID-19 “twindemic.” The data further support emerging trends about substantial geographic variation and show some ways that COVID-19 mitigation measures may have improved conditions for some populations, particularly in terms of housing security. Additionally, the results presented here indicate that further attention should be paid to the effects of first responder stress on documentation quality.

Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks

New spectroscopic surveys will increase the number of astronomical objects in need of characterisation by more than an order of magnitude. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms cannot directly estimate error, making them unsuitable for reliable science. We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infers precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra. We trained a conditional invertible neural network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key feature of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analysing this distribution, we inferred stellar parameters and their corresponding uncertainties. We carried out several tests to investigate how parameters are inferred and errors are estimated. We achieved an accuracy of 28K in $T_ eff $, 0.06 dex in $ g$, 0.03 dex in Fe/H $, and between 0.05 dex and 0.17 dex for the other abundances for high-quality spectra. Accuracy remains stable with low signal-to-noise ratio (between 5 and 25) spectra, with an accuracy of 39K in $T_ eff $, 0.08 dex in $ g$, and 0.05 dex in Fe/H $. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN. This neural network represents a compelling proposition for future astronomical surveys. These derived uncertainties are coherent and can therefore be reused in future works as Bayesian priors.

Genetic diversity insights from population genomics and machine learning tools for Nordic Arctic charr (Salvelinus alpinus) populations

Arctic charr (Salvelinus alpinus) is a salmonid species of high ecological and commercial value in the Holarctic region. Nevertheless, more information is needed about its underlying genetic diversity and population structure in the Nordics, especially regarding farmed populations. High-throughput sequencing was applied in three Arctic charr populations of anadromous or landlocked origin from Finland, Norway and Sweden. More specifically, the animals from the Swedish and Norwegian populations originated from a major egg supplier and producer, respectively. Furthermore, in the case of the Finnish population, the sampled animals originated from the only active conservation program for Arctic charr in the country with a potential interest in farming. Using double-digest restriction site-associated DNA sequencing (ddRAD-seq) on more than 500 fish, over 2000 single nucleotide polymorphisms (SNPs), both in the form of individual SNPs and as read haplotypes, were used to study the genetic diversity and structure of those populations. Genetic diversity metrics were similar between the Norwegian and the Swedish populations. However, substantially lower (40–50 %) genetic diversity was found in the Finnish population. Moreover, considerable genetic differentiation was implied between the studied populations as the mean fixation index (FST) was above 0.1 in all pairwise comparisons. All populations were easily discernible through either principal component analysis (PCA) or discriminant analysis of principal components (DAPC). In addition, unsupervised machine learning models such as K-means, Gaussian and Bayesian Gaussian mixtures were assessed for their ability to detect genetic clusters. A preceding dimensionality reduction step by PCA resulted in all three models, suggesting that the most probable number of clusters was three. Overall, our study affirmed the utility of the developed ddRAD-seq genotyping method and unveiled the genetic structure of the studied populations, both of which could contribute to their more efficient management by captive breeding.

The Impact of Digital Learning Platforms on Student Motivation in High School

This study explores the relationship between the use of digital learning platforms and student motivation in high school settings. With the increasing integration of technology in education, understanding its influence on student engagement and motivation is critical. This research employs a cross-sectional survey design with a sample of 200 high school students from New York City. Using a questionnaire based on the Academic Motivation Scale (AMS), the study examines whether students who regularly use digital platforms demonstrate higher motivation levels than their peers who do not. Data analysis, conducted using SPSS, reveals a significant positive relationship between digital platform usage and student motivation. The findings suggest that digital learning tools may enhance motivation, offering important implications for educators and policymakers. However, the study's cross-sectional nature and limited sample size present constraints, indicating the need for further research.

The development of supplemental multimedia learning modules and their impact on student learning in food biotechnology courses.

The rapid growth of online education has created opportunities to integrate multimedia learning tools into complex scientific disciplines like food biotechnology. This study aimed to develop and evaluate supplementary online course modules on gene expression analysis, protein engineering tools, and fermentation genomics for undergraduate food biotechnology education. Based on cognitive load theory and multimedia learning principles, the modules incorporated focused visual media and interactive knowledge checks. The study involved 85 students in an introductory food microbiology course and 25 students in an upper-level food biochemistry elective at a large public university. Module implementation included tracking student usage through learning management system analytics, collecting qualitative feedback, and assessing learning outcomes via exam performance. Results showed that 73%-76% of students voluntarily accessed the modules, with average engagement times of 5-8 min per module. Student feedback highlighted the modules' effectiveness in clarifying textbook content through replayable examples and real-world scenarios. Comparison of exam scores revealed a 6%-10% improvement in performance on module-related items compared to overall exam averages. Qualitative feedback indicated that students found the visual representations and interactive elements helpful for clarifying complex concepts. This study demonstrates the potential of well-designed multimedia modules to support student learning in food biotechnology education, providing a model for expanding such resources in food science curricula.

Interprofessional Education in Neurology Intensive Care Unit: Learning Activity for Students Completing Clerkships and Field Experiences.

Teaching learners the benefits and challenges of interprofessional collaborative practice (IPCP) in acute care is best done in the context of authentic patient care rather than classroom settings. Yet differing clinical schedules of students and faculty as well as structured, controlled environments of intensive care units are not conducive to bringing multiple interprofessional learners to the bedside. We developed a 2.5-hour interprofessional education (IPE) activity based around neurology intensive care unit patients that was implemented using team-based learning (TBL) methodologies, including individual/team readiness assessments (iRAT/tRAT) and application activities comprising interprofessional student-led care conferences in which authentic patients and their unique, deidentified clinical parameters were discussed. Students represented medicine, nursing, chaplain, physician assistant, social work, respiratory therapy, pharmacy, physical therapy, and law programs. Two hundred ninety-three students participated online during the first year. Mean tRAT scores were 14.9 points higher than iRAT scores (39.5 vs. 24.6 of 44 possible). There was strong agreement from students that the session met educational objectives (all >4.0 on a 5-point Likert scale). Individualized feedback was provided to learners using a rubric; 276 students shared personal reflections of knowledge learned through interactions with other health professional students/faculty. IPE activities that use authentic patient chart data as the basis for student application exercises are useful learning tools since students must teach and learn from each other, interpret data, and propose care plans based upon their collaborations. This enables students to experience acute care IPCP in a low-stakes environment.

Sustainable Management of Agrifood Systems: Interdisciplinary Workshop as a Learning Tool in Bioresource Technology in secondary and technical education in Agriculture at ECIT Riachão do Poço, Paraiba, Brazil

This article addresses the project of an Interdisciplinary Workshop, AGROTEC, and the implementation of the sensory garden at ECIT Riachão do Poço, in Paraíba, as an interdisciplinary learning tool for high school and technical students in agriculture, including the topics of bioresource technology. The municipality of Riachão do Poço, located in the state of Paraíba, with a semi-arid tropical climate, is rich in agricultural practices. The objective of the Technical Course in Agriculture at the school seeks to promote a comprehensive and contextualized education. The workshop aimed to integrate knowledge from the disciplines of plant health, agricultural soils, horticulture, olericulture and bioresource technology, providing students with practical and meaningful experiences for their training. The methodology used included holding an interactive workshop, where students could apply theories in a practical environment, encouraging collaboration and active learning. At the end, students also had a class on media content management, disseminating the work carried out in the workshop. The results demonstrated an increase in student motivation, resulting in greater learning, obtaining higher grades than at the beginning of the subjects, in addition to a greater awareness of sustainability and agroecological practices. The project not only enriched the students' education, but also strengthened the school community, demonstrating the success of the interdisciplinary approach in technical education. The experience reaffirms the importance of pedagogical innovations in the training of critical professionals prepared for the challenges of the agricultural sector, especially in the areas of bioresource technology.

Social media quality in undergraduate medical education: A reconceptualisation and taxonomy.

Social Media (SoMe) as a learning tool, though ubiquitous in society and popular within medical education, is often criticised as superficial. Its limitless output has been blamed for encouraging shorter attention spans and shirking in-depth reflection. The evidence base is itself superficial and lacking rigour or meaning. We aimed to consider a theoretical basis for how 'quality' learning may happen on such platforms. Our findings then informed the construction of a taxonomy for SoMe learning. We conducted a qualitative interview study of United Kingdom (UK) medical students using a theory-informed inductive study design. The research question was: 'How do medical students conceptualise quality of learning on social media?'. We purposively sampled participants from responses to a short survey collecting demographic and SoMe usage data. Interview data were analysed using framework analysis and informed by Blooms taxonomy, connectivism and communities of practice (CoP) theories. We received survey responses from 118 medical students across 25 UK medical schools. From these, 13 participants were recruited to individual semi-structured interviews. We constructed three themes through framework analysis of interview data: cognitive hacking, professional identity reflection and safety, control and capital. Quality SoMe learning may be conceptualised as a socially connected process, built upon constantly evolving networks but inexorably influenced by fluctuating hierarchy within learner-centric CoP. Educators and institutions may support high-quality learning for students through engagement which promotes community development, and safe, listening environments which foster professional identity formation.

Sharp Curve Trajectory Tracking of a Universal Omni-Wheeled Mobile Robot Using a Sliding Mode Controller

Abstract In our previous works, Amarasiri et al. (2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Lett. Dyn. Sys. Control 2(4), p. 040902) and Amarasiri et al. (2024, “Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots,” ASME Lett. Dyn. Sys. Control 4(2), p. 021007), two dynamic models of a universal omni-wheeled mobile robot (UOWMR), and trajectory-tracking controllers (three linear and one nonlinear) were developed and presented. The purpose of that work was to identify suitable combinations of dynamic models and trajectory-tracking controllers, choosing the combination with the highest tracking accuracy and the lowest solver execution time. The ultimate purpose is to utilize these physics-based tools in a reinforcement learning (RL) agent developed for path planning and navigation in an unstructured environment. Three trajectories were investigated including a smooth path, a sharp curved path, and a smooth path with disturbance forces. The sliding mode controller (SMC) following a trajectory with sharp (right-angled) curves was not investigated in that work. The sharp corners caused indeterminacy in the path derivatives required for the SMC algorithm. In this short article, we complete our studies of the SMC on sharp corner paths utilizing slight trajectory corner smoothing.

Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: “(audiology) AND (“artificial intelligence” OR “machine learning” OR “deep learning”)”. The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology.

KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories

We present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extraction and data mining. Central to the functionality of KnowVID-19 is its keyword-based text classification process, which plays a pivotal role in organizing and categorizing the extracted information. By employing machine learning techniques for keyword extraction—specifically RAKE, YAKE, and KeyBERT—KnowVID-19 systematically categorizes publication data into distinct topics and subtopics. This topic structuring enhances the system’s ability to match user queries with relevant research, improving both the accuracy and efficiency of the search results. In addition, KnowVID-19 leverages the NetworkX Python library to construct networks of the most relevant terms within publications. These networks are then visualized using Cytoscape software, providing a graphical representation of the relationships between key terms. This network visualization allows researchers to easily track emerging trends and developments related to COVID-19, long COVID, and associated topics, facilitating more informed and user-centered exploration of the scientific literature. KnowVID-19 also provides an interactive web application with an intuitive, user-centered interface. This platform supports seamless keyword searching and filtering, as well as a visual network of term associations to help users quickly identify emerging research trends. The responsive design and network visualization enables efficient navigation and access to targeted COVID-19 literature, enhancing both the user experience and the accuracy of data-driven insights.

SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.