Hasil untuk "History of education"

Johnny Chan, Yuming Li

This research explores the opportunities of Generative AI (GenAI) in the realm of higher education through the design and development of a multimodal chatbot for an undergraduate course. Leveraging the ChatGPT API for nuanced text-based interactions and Google Bard for advanced image analysis and diagram-to-code conversions, we showcase the potential of GenAI in addressing a broad spectrum of educational queries. Additionally, the chatbot presents a file-based analyser designed for educators, offering deep insights into student feedback via sentiment and emotion analysis, and summarising course evaluations with key metrics. These combinations highlight the crucial role of multimodal conversational AI in enhancing teaching and learning processes, promising significant advancements in educational adaptability, engagement, and feedback analysis. By demonstrating a practical web application, this research underlines the imperative for integrating GenAI technologies to foster more dynamic and responsive educational environments, ultimately contributing to improved educational outcomes and pedagogical strategies.

Moncef Derouich

This study proposes a quantitative framework to enhance curriculum coherence through the systematic alignment of Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs), contributing to continuous improvement in outcome-based education. Grounded in accreditation standards such as ABET and NCAAA, the model introduces mathematical tools that map exercises, assessment questions, teaching units (TUs), and student assessment components (SACs) to CLOs and PLOs. This dual-layer approach-combining micro-level analysis of assessment elements with macro-level curriculum evaluation-enables detailed tracking of learning outcomes and helps identify misalignments between instructional delivery, assessment strategies, and program objectives. The framework incorporates alignment matrices, weighted relationships, and practical indicators to quantify coherence and evaluate course or program performance. Application of this model reveals gaps in outcome coverage and underscores the importance of realignment, especially when specific PLOs are underrepresented or CLOs are not adequately supported by assessments. The proposed model is practical, adaptable, and scalable, making it suitable for academic programs. Its systematic structure supports institutions in implementing evidence-based curriculum improvements and provides a reliable mechanism for aligning teaching practices with desired learning outcomes. Ultimately, this framework offers a valuable tool for closing the feedback loop between instructional design, assessment execution, and learning outcomes, thus promoting greater transparency, accountability, and educational effectiveness. Institutions that adopt this model can expect to strengthen their quality assurance processes and help ensure that students graduate with the competencies required by academic standards and professional expectations.

Osama A. Marzouk, Walid Ali Marjan Haje Rhaim Jul, Amjad Masoud Khalfan Al Jabri et al.

We developed a vacuum generation system composed of a reciprocating compressor (3 tons of refrigeration) with an inverted-function that is ready to be hooked flexibly to a gas-tight container to create an evacuated enclosed atmosphere, without strict limitation of the size of that container. The evacuated container (or vacuum chamber) can serve in different purposes such as educational demonstration of the vacuum properties, extraction of perfumes from herbal resources, and preserving food. We tested the device and found it can reach a vacuum level of 26 inches of mercury in an environment with an atmospheric pressure of 28.5 inches of mercury. We compared the performance of our vacuum device to a rotary-vane vacuum pump of 1/4 horsepowers and found that the vacuum pump reaches a set test vacuum level of 25 inches of mercury before the compressor. We then demonstrated experimentally some features of the vacuum using the inverted compressor or the vane vacuum pump. These experiments serve some topics in physics for school students as well as two core subjects of mechanical engineering, namely fluid mechanics and thermodynamics.

Yuchen Wang, Pei-Duo Yu, Chee Wei Tan

Learning to learn is becoming a science, driven by the convergence of knowledge tracing, signal processing, and generative AI to model student learning states and optimize education. We propose CoTutor, an AI-driven model that enhances Bayesian Knowledge Tracing with signal processing techniques to improve student progress modeling and deliver adaptive feedback and strategies. Deployed as an AI copilot, CoTutor combines generative AI with adaptive learning technology. In university trials, it has demonstrated measurable improvements in learning outcomes while outperforming conventional educational tools. Our results highlight its potential for AI-driven personalization, scalability, and future opportunities for advancing privacy and ethical considerations in educational technology. Inspired by Richard Hamming's vision of computer-aided 'learning to learn,' CoTutor applies convex optimization and signal processing to automate and scale up learning analytics, while reserving pedagogical judgment for humans, ensuring AI facilitates the process of knowledge tracing while enabling learners to uncover new insights.

Joko Slamet

Kees de Bot’s “A History of Applied Linguistics: From 1980 to the Present”, published by Routledge in 2015, is a seminal work that meticulously traces the trajectory of applied linguistics over the past few decades. The book spans 11 chapters over approximately 168 pages, offering a detailed exploration of Applied Linguistics (AL). De Bot begins by analyzing the diverse informants who have shaped AL, considering factors like gender, race, educational backgrounds, and affiliations. He critically examines AL’s definitions, its autonomy, and its relationships with fields like TESOL and AILA. Profiles of influential leaders highlight their contributions, while a thorough review covers seminal articles and books, emphasizing publishers’ roles in research dissemination. The book explores theoretical and methodological trends, including corpus linguistics, discourse analysis, and new areas like neurolinguistics and technology in language learning. De Bot discusses psycholinguistic and sociolinguistic dimensions such as language acquisition, identity, multilingualism, and language policy. His exploration of Complex Dynamic Systems Theory (CDST) applies it to understanding language dynamics and individual differences. A citation analysis section examines publication impact and academic influence dynamics. Ultimately, De Bot reflects on AL’s broad impact on language education, from theoretical insights to practical applications.

Najla A. Barnawi, Fay A. AlAmmar, Sultan A. Aldabeis et al.

Abstract Despite the growing role of AI and robotics in healthcare, little is known about their integration into dental care for persons with disabilities (PWDs) in Saudi Arabia. This study aimed to assess dentists’ perceptions and attitudes towards and use of RT/AI in dentistry and identify the predictors of using RT/AI to care for PWDs in the Saudi context. A cross-sectional study was conducted using a previously validated online self-reported questionnaire via SurveyMonkey, targeting 309 Saudi and non-Saudi licensed dentists and dental/oral health practitioners, to collect data on the following: 1) Personal and work-related characteristics, 2) Perception toward RT/AI use, 3) Attitude toward using AI and RT in dentistry, and 4) Current use of RT and AI. RT/AI use rate was calculated for each clinical aspect and each type of impairment. Logistic regression analysis was used to identify the predictors of dentists’ use of RT and AI to provide care for PWDs. Significance was set at p < 0.05. Our study revealed that 59.2% of dentists who worked with PWDs reported utilizing RT/AI in various clinical aspects. Almost one-fourth of dentists reported using RT/AI in clinical examinations (23.9%), managing complications (26.8%), and performing invasive procedures (28.6%). Nearly one-third of respondents reported using RT/AI for taking a history (30%), non-invasive procedures (31.5%), behavioral training sessions (32.9%), health education (36.2%), medical diagnosis (36.6%), diagnostic tests (38%), and treatment planning (43.7%). Over one-half (54.9%) and one-fourth (28.6%) of the dentists reported a positive perception and attitude towards RT/AI use in dentistry. However, after adjusting for possible confounders, only previous RT/AI training remained a significant predictor of RT/AI use among dentists working with PWDs (OR = 9.18, 95% CI 2.92–28.90, p < 0.001). Our study is the first in the Saudi context to investigate the use of RT and AI by dentists caring for PWDs. Previous training was associated with greater use of RT/AI in this context. Potential collaborations between dental institutes and stakeholders in the RT and AI industry are recommended.

Michael Vierhauser, Iris Groher, Tobias Antensteiner et al.

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM Computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas.

Ahmed Kharrufa, Ian G Johnson

Generative AI (GAI) is impacting teaching and learning directly or indirectly across a range of subjects and disciplines. As educators, we need to understand the potential and limitations of AI in HCI education and ensure our graduating HCI students are aware of the potential and limitations of AI in HCI. In this paper, we report on the main pedagogical insights gained from the inclusion of generative AI into a 10 week undergraduate module. We designed the module to encourage student experimentation with GAI models as part of the design brief requirement and planned practical sessions and discussions. Our insights are based on replies to a survey sent out to the students after completing the module. Our key findings, for HCI educators, report on the use of AI as a persona for developing project ideas and creating resources for design, and AI as a mirror for reflecting students' understanding of key concepts and ideas and highlighting knowledge gaps. We also discuss potential pitfalls that should be considered and the need to assess students' literacies and assumptions of GAIs as pedagogical tools. Finally, we put forward the case for educators to take the opportunities GAI presents as an educational tool and be experimental, creative, and courageous in their practice. We end with a discussion of our findings in relation to the TPACK framework in HCI.

Ehsan Latif, Yifan Zhou, Shuchen Guo et al.

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.

Ian Brooks, Laura Harrison, Mark Reeves et al.

This research explores the use of History to enhance education in the field of ICT For Sustainability ICT4S in response to a challenge from the ICT4S 2023 conference. No previous studies were found in ICT4S but the literature on History and Education for Sustainable Development is reviewed. An ICT4S lecturer collaborated with History lecturers to add an historic parallel to each weeks teaching on a Sustainable Business and Computing unit for final year undergraduate BSc Business Computing students. A list of the topics and rationale is provided. Student perceptions were surveyed before and after the teaching and semi-structured interviews carried out. A majority of students saw relevance to their degree and career. There was an increase in the proportion of students with interest in History. The paper explores the lessons learned from the interdisciplinary collaboration, including topic choice, format and perceived value. The project has enhanced the way we approach our subjects as computing and history educators. We believe this is the first empirical, survey-based study of the use of history to enhance ICT4S education. The team will extend the research to a larger unit covering a wider range of computing degrees.

Yuan-Hao Jiang, Ruijia Li, Yizhou Zhou et al.

The development of large language models has ushered in new paradigms for education. This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework. It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices, defining four types of operations: task deconstruction, self-reflection, memory processing, and tool invocation. Furthermore, it introduces related technologies such as Chain-of-Thought, Reson+Act, and Multi-Agent Debate associated with these four types of operations. The paper also discusses the ability enhancement cycle of a multi-Agent system for education, including the outer circulation for human learners to promote knowledge construction and the inner circulation for LLM-based-Agents to enhance swarm intelligence. Through collaboration and reflection, the multi-Agent system can better facilitate human learners' learning and enhance their teaching abilities in this process.

Achraf Hsain, Hamza El Housni

This research explores the integration of chatbot technology powered by GPT-3.5 and GPT-4 Turbo into higher education to enhance internationalization and leverage digital transformation. It delves into the design, implementation, and application of Large Language Models (LLMs) for improving student engagement, information access, and support. Utilizing technologies like Python 3, GPT API, LangChain, and Chroma Vector Store, the research emphasizes creating a high-quality, timely, and relevant transcript dataset for chatbot testing. Findings indicate the chatbot's efficacy in providing comprehensive responses, its preference over traditional methods by users, and a low error rate. Highlighting the chatbot's real-time engagement, memory capabilities, and critical data access, the study demonstrates its potential to elevate accessibility, efficiency, and satisfaction. Concluding, the research suggests the chatbot significantly aids higher education internationalization, proposing further investigation into digital technology's role in educational enhancement and strategy development.

Kaska Porayska-Pomsta

This paper examines the historical foundations, current practices, and emerging challenges for Artificial Intelligence in Education (AIED) within broader AI practices. It highlights AIED's unique and rich potential for contributing to the current AI policy and practices, especially in the context of responsible AI. It also discusses the key gaps in the AIED field, which need to be addressed by the community to elevate the field from a cottage industry to the level where it will deservedly be seen as key to advancin AI research and practical applications. The paper offers a five-point manifesto aimed to revitalise AIED' contributions to education and broader AI community, suggesting enhanced interdisciplinary collaboration, a broadened understanding of AI's impact on human functioning, and commitment to setting agendas for human-centred educational innovations.This approach positions AIED to significantly influence educational technologies to achieve genuine positive impact across diverse societal segments.

Anvit Sinha, Shruti Goyal, Zachary Sy et al.

Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). This contribution enhances the efficiency and effectiveness of instructional support and significantly improves the quality and speed of responses provided to students, thereby enriching the overall learning experience. Background: Grounded in Vygotsky's socio-cultural theory and the concept of the More Knowledgeable Other (MKO), the study examines how GenAI can act as an auxiliary MKO to enrich educational dialogue between students and instructors. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Using a mixed-methods approach in large introductory programming courses, human Teaching Assistants (AI-TAs) employed an AI-assisted platform to pre-answer student queries. We analyzed efficiency indicators like the frequency of modifications to AI-generated responses and gathered qualitative feedback from AI-TAs. Findings: The findings indicate no significant difference in student reception to responses generated by AI-TAs compared to those provided by human instructors. This suggests that GenAI can effectively meet educational needs when adequately managed. Moreover, AI-TAs experienced a reduction in the cognitive load required for responding to queries, pointing to GenAI's potential to enhance instructional efficiency without compromising the quality of education.

Joshua Okyere, Castro Ayebeng, Sylvia Ahinee Adjedu et al.

Abstract Background There is a strong evidence showing that women who start menstruation early are at a greater risk of developing breast cancer. Recognizing that women will seek breast cancer screening when they have a high perceived risk, we hypothesized that women who experienced early menarche will be more likely to utilize clinical breast examination (CBE). Hence, we aimed to investigate the association between age at first menstruation and women’s utilization of CBE in Côte d'Ivoire. Methods We used data from the 2021 Côte d'Ivoire demographic and health survey. A sample of 14,685 women was used for the analysis. A descriptive analysis, as well as bivariate and multivariate logistic regression models were computed in STATA version 18. Adjusted odds ratio (AOR) and a 95% confidence interval was used to present the result. Results CBE utilization was 17.4%. Women who had their first menstruation before attaining 15 years were significantly less likely to utilize CBE services [AOR = 0.89; 95% CI 0.81–0.99]. A significantly higher utilization of CBE was found among those with primary [AOR = 1.48, 95% CI 1.29–1.70], secondary [AOR = 2.96, 95% CI 2.59–3.38], and higher education [AOR = 4.35, 95% CI 3.50–5.40] compared to those with no formal education. Increasing likelihood of CBE utilization was observed as age increased. Rural residence was associated with lower odds of CBE utilization (AOR = 0.84, 95% CI 0.74–0.95]. Increasing wealth status was associated with higher odds of CBE utilization with those in the richest households having the highest odds compared to women in the poorest household [AOR = 2.11, 95% CI 1.69–2.64]. Conclusion Utilization of CBE is low among women of reproductive age in Côte d'Ivoire. We conclude that even though existing literature has established early age at first menstruation as a strong risk factor for breast cancer, CBE utilization is significantly low among those who had early menarche. Going forward, it is necessary for Côte d'Ivoire’s health Ministry to intensify breast cancer awareness in the country. Such awareness campaigns must emphasize age at menarche as a risk factor so as to motivate women with a history of early menstruation to utilize CBE.

Ahmed Ali Hassan, Abdullah Al-Nafeesah, Ishag Adam et al.

Objectives There is an increasing trend of pre-diabetes and diabetes mellitus (DM) among adolescents, and sub-Saharan Africa is no exception. However, few published data on pre-diabetes among adolescents in Sudan exist. We aimed to investigate the prevalence of and factors associated with pre-diabetes among adolescents in Eastern Sudan.Design A community-based cross-sectional study was conducted from August to October 2023.Settings This community-based study was conducted in Gadarif city, the capital of Gadarif state, Eastern Sudan.Participants Adolescents (within the ages of 10–19 years).Main outcome measures A questionnaire was used to collect socio-demographic information. Anthropometric and glycated haemoglobin (HbA1c) measurements were performed in accordance with standard procedures. Multivariate logistic regression analysis was performed.Results Of the 387 enrolled adolescents, 207 (53.5%) were female and 180 (46.5%) were male. The median (IQR) age was 14.0 (12.0–16.0) years. 39.5% of the participants’ fathers were employed. The median (IQR) HbA1c was 5.5% (5.2%–5.8%). One-third (32.6%) of the adolescents had pre-diabetes or DM. Of the participants, 67.4%, 30.0% and 2.6% had no DM, pre-diabetes or type 2 DM, respectively. In the univariate analysis, the father’s employment (OR=1.60, 95% CI=1.03 to 2.50) was associated with increased odds of pre-diabetes; age, sex, parents’ education, the mother’s occupation, body mass index z-score, cigarette smoking and a family history of DM were not associated with pre-diabetes. In the multivariate analysis, the father’s employment (adjusted OR=1.70, 95% CI=1.03 to 2.50) was associated with increased odds of pre-diabetes.Conclusion Pre-diabetes is a significant public health problem among adolescents in Eastern Sudan. The introduction of early screening programmes for pre-diabetes at the community level is recommended to halt the progression of pre-diabetes to DM and to deal with existing DM among adolescents.

Roozbeh Aliabadi, Aditi Singh, Eryka Wilson

The integration of artificial intelligence (AI) into education has the potential to transform the way we learn and teach. In this paper, we examine the current state of AI in education and explore the potential benefits and challenges of incorporating this technology into the classroom. The approaches currently available for AI education often present students with experiences only focusing on discrete computer science concepts agnostic to a larger curriculum. However, teaching AI must not be siloed or interdisciplinary. Rather, AI instruction ought to be transdisciplinary, including connections to the broad curriculum and community in which students are learning. This paper delves into the AI program currently in development for Neom Community School and the larger Education, Research, and Innovation Sector in Neom, Saudi Arabia s new megacity under development. In this program, AI is both taught as a subject and to learn other subjects within the curriculum through the school systems International Baccalaureate (IB) approach, which deploys learning through Units of Inquiry. This approach to education connects subjects across a curriculum under one major guiding question at a time. The proposed method offers a meaningful approach to introducing AI to students throughout these Units of Inquiry, as it shifts AI from a subject that students like or not like to a subject that is taught throughout the curriculum.

Ovidiu Gherman, Cristina Elena Turcu, Corneliu Octavian Turcu

Current changes in society and the education system, cumulated with the accelerated development of new technologies, entail inherent changes in the educational process. Numerous studies have shown that the pandemic has forced a rapid transformation of higher education. Thus, if before the pandemic digital technologies were used to supplement the learning process, now they are the main means of learning delivery. In addition, as previous research has shown, new pedagogical strategies and new ways of teaching and learning are needed for the current generation of students, the so-called Generation Z, to acquire new knowledge and develop new skills. In this necessary evolution of the educational process, a possible solution to increase the effectiveness of the learning process for the Generation Z students is to use microlearning to extend the traditional ways of learning. Many studies have shown that microlearning, based on how today's students learn and memorize, facilitates learning. In recent years there has been a growing trend in their use of microlearning in the educational process. But, in order to be effective, this approach must allow the individual knowledge building, by indicating a guiding direction of the optimal path to achieve the proposed objectives. We propose a system for personalized learning using microlearning, which provides support and guidance to students based on their individual needs, in order to increase their interest in learning, but also to compensate for various deficiencies in their educational background. We also present a case study from the higher education sector. Feedback from students and data collected during the semester as a result of the students' behavioural analysis and their real learning motivations will be used to improve the proposed system.

Pantea Birang Khojastehpour, Fariborz Dortaj, Fatemeh Ghaemi

This research was conducted with the aim of mediating the role of job motivation in the relationship between stress coping strategies and social-emotional competence in primary teachers. The cross-sectional research method is correlational. The statistical population of the present study was made up of all the teachers who were teaching in the elementary school of Tehran in 1400-1401, and among them, 350 people were selected by multi-stage cluster sampling method. In this research, tools of coping strategies (Lazarus and Folkman, 1985), job motivation (Robinson, 2004) and social-emotional competence (Boyatzis, 2007) were used, all of which had acceptable validity and reliability. In order to analyze the data, SPSS-V23 and Lisrel-V7.8 software were used. In order to respond to the research hypotheses, structural equation modeling was used. The research findings showed that the model has a good fit. The results showed that strategies to deal with stress have a direct effect on teachers' social-emotional competencies (p<0.05). Strategies to deal with stress have an indirect effect on primary teachers' job motivation (p<0.05). Career motivation has an indirect effect on social-emotional competencies of elementary teachers (p<0.05). Also, the results showed that the indirect effect of stress coping strategies with social-emotional competencies through the mediation of job motivation in primary teachers was confirmed with 95% confidence. Therefore, paying attention to the mentioned variables helps researchers and therapists in prevention and designing more suitable treatments.Key words: social-emotional competence, strategies to deal with stress, job motivation

Gedefaw Diress, Seteamlak Adane, Melese Linger et al.

<h4>Background</h4>In Ethiopia, HIV/AIDS continues to be a major public health problem mostly due to the high prevalence of risky sexual behaviors. However, research on risky sexual behavior and its determinants among unmarried men (never married, widowed, and divorced) who are highly vulnerable to risky sexual behavior was limited. Therefore, this study aimed to assess the magnitude of risky sexual behavior and its determinants among non-married men using a nationally representative sample.<h4>Methods</h4>The analysis was done on 5680 sexually active unmarried men aged 15-59 years using data from the 2016 Ethiopia Demographic Health Survey (EDHS). The main outcome variable was risky sexual behavior which defined as having at least one of the following: multiple sexual partners; initiation of sex before the age of 18 years; inconsistent condom use in the last 12 months; alcohol consumption at last sex. Multivariable generalized linear mixed-effects regression was employed to identify variables associated with risky sexual behavior.<h4>Result</h4>The overall magnitude of risky sexual behavior was 26.9% (95% CI; 25.7, 28.0). Currently employed (AOR = 2.49, 95% CI = 1.64-3.77), history of HIV testing (AOR = 2.51, 95% C = 1.95-3.23), drinking alcohol almost every day (AOR = 5.49, 95 CI = 2.73-11.02), and using Internet daily (AOR = 1.99, 95% CI = 1.06-3.74) increase the odds of risky sexual behavior. Whereas, primary education (AOR = 0.44, 95% CI = 0.32-0.61), secondary education level (AOR = 0.46, 95% CI = 0.29-0.72) and a high proportion of community-level media exposure (AOR = 0.42, 95% CI = 0.12-0.75) decrease the odds of risky sexual behavior.<h4>Conclusion</h4>In general, a significant proportion of sexually active unmarried men in Ethiopia have practiced risky sexual behavior. An intervention should be designed which are against the factors found to increase the odds of risky sexual behavior to reduce the incidence of HIV and other sexually transmitted infections.