Artificial Language Research Articles

Far transfer of retrieval-practice benefits: rule-based learning as the underlying mechanism.

Benefits of self-testing for learning have been consistently shown for simple materials such as word lists learned by rote memorization. Considerably less evidence for such benefits exists for complex, more educationally relevant materials and its application to new situations. The present study explores the mechanisms underlying this transfer. To this end, a typical retrieval-practice-effect paradigm was applied to foster the learning of an artificial language. Participants either repeatedly studied grammatically correct exemplar sentences of the artificial language or engaged in a cloze test as the interim testafter learning. To assess far transfer, participants in both groups of restudy and retrieval practice engaged in a grammaticality judgment test after a delay of 5min and 1week. In addition, participants in both groups completed a final memory test (i.e., a cloze test identical to the initial test) 1week after learning. In addition to a long-term memory benefit of retrieval practice,results revealed alsoa retrieval-practicebenefit in the far-transfer test after the 1-week delay. The findings further support the view that far transfer is supported by learning the underlying grammatical rules as opposed to memorizing the material. Thus, retrieval practice is also effective for fostering learning of complex materials and, even more importantly, for promoting transfer of learning-a crucial goal in modern educational practices.

Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences.

Studies are exploring ways to improve medication adherence, with sentiment analysis (SA) being an underutilized innovation in pharmacy. This technique uses artificial intelligence (AI) and natural language processing to assess text for underlying feelings and emotions. This study aimed to evaluate the use of two SA models, Valence Aware Dictionary for Sentiment Reasoning (VADER) and Emotion English DistilRoBERTa-base (DistilRoBERTa), for the identification of patients' sentiments and emotions towards their pharmacotherapy. A dataset containing 320,095 anonymized patients' reports of experiences with their medication was used. VADER assessed sentiment polarity on a scale from - 1 (negative) to + 1 (positive). DistilRoBERTa classified emotions into seven categories: anger, disgust, fear, joy, neutral, sadness, and surprise. Performance metrics for the models were obtained using the sklearn.metrics module of scikit-learn in Python. VADER demonstrated an overall accuracy of 0.70. For negative sentiments, it achieved a precision of 0.68, recall of 0.80, and an F1-score of 0.73, while for positive sentiments, it had a precision of 0.73, recall of 0.59, and an F1-score of 0.65. The AUC for the ROC curve was 0.90. DistilRoBERTa analysis showed that higher ratings for medication effectiveness, ease of use, and satisfaction corresponded with more positive emotional responses. These results were consistent with VADER's sentiment analysis, confirming the reliability of both models. VADER and DistilRoBERTa effectively analyzed patients' sentiments towards pharmacotherapy, providing valuable information. These findings encourage studies of SA in clinical pharmacy practice, paving the way for more personalized and effective patient care strategies.

Chunk Duration Limits the Learning of Multiword Chunks: Behavioral and Electroencephalogram Evidence from Statistical Learning.

Language comprehension involves the grouping of words into larger multiword chunks. This is required to recode information into sparser representations to mitigate memory limitations and counteract forgetting. It has been suggested that electrophysiological processing time windows constrain the formation of these units. Specifically, the period of rhythmic neural activity (i.e., slow-frequency neural oscillations) may set an upper limit of 2-3 sec. Here, we assess whether learning of new multiword chunks is also affected by this neural limit. We applied an auditory statistical learning paradigm of an artificial language while manipulating the duration of to-be-learnt chunks. Participants listened to isochronous sequences of disyllabic pseudowords from which they could learn hidden three-word chunks based on transitional probabilities. We presented chunks of 1.95, 2.55, and 3.15 sec that were created by varying the pause interval between pseudowords. In a first behavioral experiment, we tested learning using an implicit target detection task. We found better learning for chunks of 2.55 sec as compared to longer durations in line with an upper limit of the proposed time constraint. In a second experiment, we recorded participants' electroencephalogram during the exposure phase to use frequency tagging as a neural index of statistical learning. Extending the behavioral findings, results show a significant decline in neural tracking for chunks exceeding 3 sec as compared to both shorter durations. Overall, we suggest that language learning is constrained by endogenous time constraints, possibly reflecting electrophysiological processing windows.

Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077-0.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real-world scenarios.

The Potential of Using Generative AI/NLP to Identify and Analyse Critical Incidents in a Critical Incident Reporting System (CIRS): A Feasibility Case-Control Study.

To enhance patient safety in healthcare, it is crucial to address the underreporting of issues in Critical Incident Reporting Systems (CIRSs). This study aims to evaluate the effectiveness of generative Artificial Intelligence and Natural Language Processing (AI/NLP) in reviewing CIRS cases by comparing its performance with human reviewers and categorising these cases into relevant topics. A case-control feasibility study was conducted using CIRS cases from the German CIRS-Anaesthesiology subsystem. Each case was reviewed by a human expert and by an AI/NLP model (ChatGPT-3.5). Two CIRS experts blindly assessed these reviews, rating them on linguistic quality, recognisable expertise, logical derivability, and overall quality using six-point Likert scales. On average, the CIRS experts correctly classified 80% of human CIRS reviews as created by a human and misclassified 45.8% of AI reviews as written by a human. Ratings on a scale of 1 (very good) to 6 (failed) revealed a comparable performance between human- and AI-generated reviews across the dimensions of linguistic expression (p = 0.39), recognisable expertise (p = 0.89), logical derivability (p = 0.84), and overall quality (p = 0.87). The AI model was able to categorise the cases into relevant topics independently. This feasibility study demonstrates the potential of generative AI/NLP in analysing and categorising cases from the CIRS. This could have implications for improving incident reporting in healthcare. Therefore, additional research is required to verify and expand upon these discoveries.

Experimental assessment of the performance of artificial intelligence in solving multiple-choice board exams in cardiology.

The aim of the present study was to evaluate the performance of various artificial intelligence (AI)-powered chatbots (commercially available in Switzerland up to June 2023) in solving a theoretical cardiology board exam and to compare their accuracy with that of human cardiology fellows. For the study, a set of 88 multiple-choice cardiology exam questions was used. The participating cardiology fellows and selected chatbots were presented with these questions. The evaluation metrics included Top-1 and Top-2 accuracy, assessing the ability of chatbots and fellows to select the correct answer. Among the cardiology fellows, all 36 participants successfully passed the exam with a median accuracy of 98% (IQR 91-99%, range from 78% to 100%). However, the performance of the chatbots varied. Only one chatbot, Jasper quality, achieved the minimum pass rate of 73% correct answers. Most chatbots demonstrated a median Top-1 accuracy of 47% (IQR 44-53%, range from 42% to 73%), while Top-2 accuracy provided a modest improvement, resulting in a median accuracy of 67% (IQR 65-72%, range from 61% to 82%). Even with this advantage, only two chatbots, Jasper quality and ChatGPT plus 4.0, would have passed the exam. Similar results were observed when picture-based questions were excluded from the dataset. Overall, the study suggests that most current language-based chatbots have limitations in accurately solving theoretical medical board exams. In general, currently widely available chatbots fell short of achieving a passing score in a theoretical cardiology board exam. Nevertheless, a few showed promising results. Further improvements in artificial intelligence language models may lead to better performance in medical knowledge applications in the future.

EXPLORING GEMINI AI: REVOLUTIONIZING CONVERSATIONAL AGENTS WITH MULTI-MODAL INTELLIGENCE

The swift advancement of artificial intelligence and natural language processing has resulted in the creation of highly advanced conversational agents, often referred to as AI chatbots. This research focuses on designing and evaluating a chatbot application based on the capabilities of Gemini, Google DeepMind’s cutting-edge language model. The Gemini model utilizes cutting-edge machine learning methods, including transformers and multi-modal processing, to improve its comprehension and generation of natural language across diverse contexts.

S276 Optical Accuracy of Artificial Intelligence Large Language Models in Classifying Colorectal Polyps Based on Shape, Size, and Histology, Using Endoscopic Images

S276 Optical Accuracy of Artificial Intelligence Large Language Models in Classifying Colorectal Polyps Based on Shape, Size, and Histology, Using Endoscopic Images

Analysis of ChatGPT in the Triage of Common Spinal Complaints

ChatGPT is a natural language processing chatbot with a significant prevalence in modern media with a clear application in the medical triage workflow. ChatGPT has shown significant capacity for understanding clinical vignettes, radiology reports, and even passing the American Board of Neurological Surgery board examination. There has never been an evaluation of the chatbot in triage and diagnosing spinal vignettes common to primary and urgent care practice. Fifteen clinical scenarios were created to mimic spinal complaints common to primary and urgent care scenarios. GPT-4 was instructed to assess the situation as if it was in a primary care office and determine diagnosis, imaging recommendations, and if emergency room (ER) or operative referral was necessary. Answers were recorded and the results compared to those of attending and resident respondents. GPT-4 provided the most likely diagnosis in each scenario. Additionally, it recommended reasonable clinical management of each scenario which would fall within standard practice guidelines. ChatGPT tended toward over-referral to the ER; however, this was not significant. GPT-4 was noninferior in all categories when compared to respondents. ChatGPT is a powerful tool for primary triage of spinal issues. It can rapidly and accurately evaluate clinical scenarios and provide clear diagnostic reasoning. GPT-4 is not designed for medical use and will provide a disclaimer as such. It did tend toward over-referring patients to the ER. With specific training, it is likely that artificial intelligence and natural language processing chatbots will become widely used in primary triage of spinal issues.

A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology.

Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.

Using Artificial Intelligence to Support Informed Decision-Making on BRAF Mutation Testing.

Precision oncology relies on accurate and interpretable reporting of testing and mutation rates. Focusing on the BRAFV600 mutations in advanced colorectal carcinoma, non-small-cell lung carcinoma, and cutaneous melanoma, we developed a platform displaying testing and mutation rates reported in the literature, which we annotated using an artificial intelligence (AI) and natural language processing (NLP) pipeline. Using AI, we identified publications that likely reported a testing or mutation rate, filtered publications for cancer type, and identified sentences that likely reported rates. Rates and covariates were subsequently manually curated by three experts. The AI performance was evaluated using precision and recall metrics. We used an interactive platform to explore and present the annotated testing and mutation rates by certain study characteristics. The interactive dashboard, accessible at the BRAF dimensions website, enables users to filter mutation and testing rates with relevant options (eg, country of study, study type, mutation type) and to visualize annotated rates. The AI pipeline demonstrated excellent filtering performance (>90% precision and recall for all target cancer types) and moderate performance for sentence classification (53%-99% precision; ≥75% recall). The manual annotation of testing and mutation rates revealed inter-rater disagreement (testing rate, 19%; mutation rate, 70%), indicating unclear or nonstandard reporting of rates in some publications. Our AI-driven NLP pipeline demonstrated the potential for annotating biomarker testing and mutation rates. The difficulties we encountered highlight the need for more advanced AI-powered literature searching and data extraction, and more consistent reporting of testing rates. These improvements would reduce the risk of misinterpretation or misunderstanding of testing and mutation rates by AI-based technologies and the health care community, with beneficial impacts on clinical decision-making, research, and trial design.

Language learning technologies: A review of trends in the USA and globally

This review provides a succinct overview of the comprehensive review on language learning technologies, exploring key trends in both the United States and global contexts. The review delves into the transformative impact of technology on language education, examining emerging trends that shape the landscape of language learning methodologies. In recent years, language learning technologies have witnessed a remarkable evolution, revolutionizing traditional approaches to language education. This review critically analyzes trends in language learning technologies, focusing on their applications, effectiveness, and implications for language learners, with a specific emphasis on both the United States and global perspectives. The exploration of language learning technologies in the United States reveals a dynamic landscape characterized by innovative tools, platforms, and methodologies. From interactive language apps to virtual reality language immersion experiences, the USA has been at the forefront of adopting cutting-edge technologies to enhance language acquisition. The review assesses the success and challenges of these technologies in diverse educational settings, shedding light on their integration into formal education systems and informal learning environments. On a global scale, the review provides insights into how language learning technologies are shaping language education practices across different countries and regions. It examines the adoption of technology in diverse cultural and linguistic contexts, exploring the effectiveness of digital language learning resources in overcoming linguistic barriers and promoting multilingualism. Key trends identified in the review include the rise of artificial intelligence-driven language tutors, the gamification of language learning, and the increasing emphasis on personalized, adaptive learning experiences. Additionally, the review explores the impact of technology on fostering cultural competence and global communication skills, essential components of language proficiency in the interconnected world. The findings of this review contribute to a deeper understanding of the current state of language learning technologies, offering valuable insights for educators, policymakers, and researchers. By examining trends in the USA and globally, the review provides a comprehensive perspective on the role of technology in shaping the future of language education, paving the way for informed decisions and advancements in language learning methodologies.

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.

Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

THE EDUCATIONAL USE OF INTERACTIVE ELECTRONIC TEXTBOOKS IN PRIMARY FOREIGN LANGUAGE TEACHING

This article is devoted to the problem of developing foreign language communicative competence of primary school students on the basis of an interactive electronic textbook and provides an example of a training course developed based on an electronic textbook. The specificity of teaching a foreign language lies in the fact that students master the language in an artificial language environment due to the lack of a natural one. New information technologies are not only new technical means, but also new forms and methods of teaching, a new approach to the learning process. This situation involves the widespread use of computer technology and various technical teaching aids. The use of computer technology in foreign language lessons in primary school can be presented in different variations: working with specialized sites, watching authentic videos, etc. А very effective and relatively affordable method for teaching foreign languages is using of the electronic textbook. An electronic textbook allows you to implement the principles of a differentiated and individual approach to learning. Therefore, it is quite natural that in the teaching of a foreign language, new opportunities for using information and communication technologies have found a wide variety of applications. Despite the presence of various approaches to the problem of implementation new learning technologies in primary foreign language education, the educational use of electronic textbooks in primary ELT requires a more complete consideration.

Chat GPT and Creative Writing: Experiences of Master's Students in Enhancing

Improving writing skills at all levels of education is a fundamental goal of education. ChatGPT, as an artificial intelligence-based language model, is recognized as a significant tool in the development of this skill. The primary objective of this research is to describe the impact of ChatGPT on the creative writing processes, as perceived by master's students. In line with this goal, the research utilizes the semi-structured interview technique as part of qualitative data collection methods. The study group comprises master's students enrolled in a teacher training program at a state university in the Central Anatolia Region during the 2022-2023 academic year. The study group is formed through purposive sampling. Data related to the research are obtained through a semi-structured interview form developed by the researcher. The collected data are analyzed using content analysis, a method frequently employed in qualitative research. Accordingly, the research findings indicate that ChatGPT is an effective tool in enhancing creative writing skills. It accelerates the writing process for students, provides them with new ideas, and boosts their writing motivation.

The performance of artificial intelligence-based large language models on ophthalmology-related questions in Swedish proficiency test for medicine: ChatGPT-4 omni vs Gemini 1.5 Pro

PurposeTo compare the interpretation and response context of two commonly used artificial intelligence (AI)-based large language model (LLM) platforms to ophthalmology-related multiple choice questions (MCQs) in the Swedish proficiency test for medicine (“kunskapsprov för läkare”) exams. DesignObservational study. MethodsThe questions of a total of 29 exams held between 2016 and 2024 were reviewed. All ophthalmology-related questions were included in this study, and categorized into ophthalmology sections. Questions were asked to ChatGPT-4o and Gemini 1.5 Pro AI-based LLM chatbots in Swedish and English with specific commands. Secondly, all MCQs were asked again without feedback. As the final step, feedback was given for questions that were still answered incorrectly, and all questions were subsequently re-asked. ResultsA total of 134 ophthalmology-related questions out of 4876 MCQs were evaluated via both AI-based LLMs. The MCQ count in the 29 exams was 4.62 ± 2.21 (range: 0–8). After the final step, ChatGPT-4o achieved higher accuracy in Swedish (94 %) and English (95.5 %) compared to Gemini 1.5 Pro (both at 88.1 %) (p = 0.13, and p = 0.04, respectively). Moreover, ChatGPT-4o provided more correct answers in the neuro-ophthalmology section (n = 47) compared to Gemini 1.5 Pro across all three attempts in English (p<0.05). There was no statistically significant difference either in the inter-AI comparison of other ophthalmology sections or in the inter-lingual comparison within AIs. ConclusionBoth AI-based LLMs, and especially ChatGPT-4o, appear to perform well in ophthalmology-related MCQs. AI-based LLMs can contribute to ophthalmological medical education not only by selecting correct answers to MCQs but also by providing explanations.

Candidates resume analyzer using artificial intelligence

In the modern recruitment process, where the number of candidates and job vacancies constantly increases, automating resume analysis becomes critically important for efficient selection. This paper focuses on developing a system for automated resume analysis using artificial intelligence, specifically the OpenAI model (ChatGPT). The paper thoroughly examines the system's components, structure, and functional capabilities. The system consists of several key modules: the user interface ensures interaction with users, the Application Programming Interface Gateway facilitates data exchange between the interface and the server, and the authentication module controls system access, ensuring user security. The main part of the system's business logic is implemented on the server, which interacts with the natural language processing module, utilizing the ChatGPT model for processing resume text data. The use of natural language processing technologies allows the system to automatically extract and analyse key elements of resumes, such as education, work experience, skills, and certifications of candidates. This significantly speeds up the processing of large volumes of resumes, improving selection efficiency and accuracy. Data obtained during the analysis is stored in a PostgreSQL database, ensuring reliable storage and quick access for further processing. The paper also emphasizes the importance of integrating the system with other recruiting platforms and databases for more efficient management of the hiring process. Such integration allows for automatic synchronization of job vacancies and candidates, optimizing the search and selection process. Additionally, the paper highlights the key challenges associated with automating resume processing, such as the diversity of resume formats submitted by candidates, differences in data structure, and potential errors in automatically recognizing important information. Despite these challenges, the use of artificial intelligence and natural language processing methods significantly enhances the productivity and accuracy of recruitment processes. Thus, the developed automated resume analysis system based on Artificial Intelligence can become a significant tool in the modern candidate selection process. Its functionality not only enables quick and accurate resume analysis but also allows integration with other HR systems, simplifying the recruitment process, reducing data processing time, and improving selection accuracy.

Advancement of Generative Pre-trained Transformer Chatbots in Answering Clinical Questions in the Practical Rhinoplasty Guideline.

The Generative Pre-trained Transformer (GPT) series, which includes ChatGPT, is an artificial large language model that provides human-like text dialogue. This study aimed to evaluate the performance of artificial intelligence chatbots in answering clinical questions based on practical rhinoplasty guidelines. Clinical questions (CQs) developed from the guidelines were used as question sources. For each question, we asked GPT-4 and GPT-3.5 (ChatGPT), developed by OpenAI, to provide answers for the CQs, Policy Level, Aggregate Evidence Quality, Level of Confidence in Evidence, and References. We compared the performance of the two types of artificial intelligence (AI) chatbots. A total of 10 questions were included in the final analysis, and the AI chatbots correctly answered 90.0% of these. GPT-4 demonstrated a lower accuracy rate than GPT-3.5 in answering CQs, although without statistically significant difference (86.0% vs. 94.0%; p = 0.05), whereas GPT-4 showed significantly higher accuracy for the level of confidence in Evidence than GPT-3.5 (52.0% vs. 28.0%; p < 0.01). No statistical differences were observed in Policy Level, Aggregate Evidence Quality, and Reference Match. In addition, GPT-4 rated significantly higher in presenting existing references than GPT-3.5 (36.9% vs. 24.1%; p = 0.01). The overall performance of GPT-4 was similar to that of GPT-3.5. However, GPT-4 provided existing references at a higher rate than GPT-3.5. GPT-4 has the potential to provide a more accurate reference in professional fields, including rhinoplasty. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial intelligence large language model scores highly on focused practice designation in metabolic and bariatric surgery board practice questions.

Artificial intelligence models such as ChatGPT (Open AI) have performed well on the exams of various medical and surgical fields. It is not yet known how ChatGPT performs on similar metabolic and bariatric surgery (MBS) questions. Assess the performance of ChatGPT on Focused Practice Designation in Metabolic and Bariatric Surgery board-style questions. United States. Questions obtained from the largest commercially available bank of FPD-MBS practice questions were entered into ChatGPT-4, as is, without prior training. We assessed the overall percentage correct as well as the percentage correct within each of the five American Board of Surgery (ABS) question categories. One-way ANOVA was used to determine if the frequency of correct answers differed between categories. Out of 255 questions, ChatGPT-4 correctly answered 189 (74.1%). Between the five question categories there was no difference between the frequency of correct answers (p = 0.22). It did not matter if questions were entered individually or in groups of up to 10. Without prior training, ChatGPT-4 scored highly when evaluated on the largest practice question bank for the FPD-MBS exam.

Bringing Artificial Intelligence to Research Analytics: Research Highlighter-MatchMaker

It is more significant to identify accurate grant opportunities given the increasing challenges in securing grants these days. Combining Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques, we have developed Research Highlighter-MatchMaker to facilitate the grant application process. The tool aims to provide an overview of research strength and to recommend matched funding opportunities as well as potential collaborators for researchers based on their research track records. It has the potential to expand to provide matching for on-campus researchers and industrial partners. It will also include a chatbot to facilitate the research administration infrastructure and process on campus.