SOCIAL WORK STUDENTS’ PERCEPTIONS OF ARTIFICIAL INTELLIGENCE: A PHENOMENOLOGICAL STUDY

The study aimed to assess teachers' perceptions of artificial intelligence (AI), their understanding of its potential advantages and disadvantages, and the specific training needs necessary for successful AI integration. The study aimed to assess teachers' perceptions of artificial intelligence (AI), their understanding of its potential advantages and disadvantages, and the specific training needs necessary for successful AI integration. A mixed-method research design was used in collecting and analyzing quantitative and qualitative data. Descriptive statistics was utilized to delineate teachers' perspectives on AI in education. Open-ended questions were employed to gather insights into the challenges teachers encounter when using AI in the classroom. The findings revealed that teachers' experiences with AI present opportunities and challenges in integrating technology into the classroom. While teachers expressed optimism about AI's potential to enhance education, citing benefits such as personalized learning, educator support, and administrative efficiency, they also voiced concerns about increased workload, excessive student reliance on technology, algorithmic bias within AI systems, and data privacy. A balanced approach to AI integration is recommended not only that complements traditional teaching methods and addresses privacy and algorithmic bias. Schools are encouraged to offer comprehensive professional development programs for teachers to introduce AI technologies, address the ethical and practical challenges associated with their use in education, ensure equitable access to AI tools, and improve technological infrastructure. Keywords: Artificial Intelligence, Capacity building ,Educators, Education, private schools,

This study examines teachers’ perceptions of Artificial Intelligence (AI) integration in education, with a particular focus on its impact on cultural literacy and its contributions to the Sustainable Development Goals (SDGs). The study employs a quantitative descriptive design, involving 25 teachers from SD Islam Prihaswari in East Lombok, Indonesia, who were selected through purposive sampling. Data were collected between July and August 2025 using a Likert-scale questionnaire consisting of 20 items distributed across four dimensions: AI utilization, cultural awareness, contribution to SDGs, and readiness and challenges. Data analysis employed descriptive statistics, including mean scores, standard deviations, percentage indices, 95% confidence intervals, and categorical interpretations. The results indicate an overall positive perception of AI integration in education. The highest mean score was observed in the SDGs dimension (M = 4.90, SD = 0.30, CI95% = 4.78–5.02), while the utilization dimension received the lowest score (M = 4.20, SD = 0.65, CI95% = 4.03–4.37). These findings suggest that teachers recognize AI’s potential to strengthen cultural literacy and support sustainable education, although practical implementation remains constrained by infrastructural and pedagogical limitations. The study highlights the need for improved digital infrastructure, systematic teacher training, and culturally responsive AI-based learning models to ensure meaningful and sustainable integration of AI in education

This study explored university students’ perceptions of artificial intelligence (AI) literacy and AI education. Specifically, the authors sought to understand the level of AI literacy among university students, the extent of their prior exposure to AI education, and the factors that affect their AI education. The study was conducted through an online survey distributed among 300 university students at a Korean university. The study findings indicate that most university students are interested in learning AI. Moreover, the results showed that students with prior experience with software (SW) education showed superior knowledge, a better grasp of AI concepts, and more confidence in using AI technologies, especially in computer coding skills. On the other hand, students with less exposure to prior AI education expressed a need for more learning opportunities and sufficient knowledge of computer coding skills. In addition, we found that students who had received SW education during their K-12 schooling showed better math skills than those who had only received short-term training in university. These findings underscore the importance of addressing the educational barriers that impede university students’ ability to harness this interest effectively. In conclusion, our study provides valuable insights into university students’ perceptions regarding AI literacy and the factors that affect their AI education.

IntroductionRadiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. MethodsA Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. Results86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. ConclusionsResponses indicate that AI will have a strong impact on reporting radiographers’ decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. Implications for practiceThis survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile. The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education. This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview. The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others (P < 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic. Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified.

Background: Artificial Intelligence (AI), recognized as the fourth industrial revolution, is revolutionizing medical practice through enhanced diagnostics, treatment planning, and healthcare delivery. Its expanded role post-COVID-19 underscores the urgency of AI literacy among future physicians. This study aimed to explore medical students’ perceptions of AI in medical education. Method: A cross-sectional study was conducted from June to July 2024 at CMH and AIMC medical colleges in Pakistan. Using a non-probability convenience sampling technique, an online questionnaire was distributed among MBBS students from both public and private institutions. A minimum of 10% of students from each academic year were invited, yielding a final sample of 139 respondents (response rate: 92%). Associations between categorical variables were analyzed using the chi-square test. Results: Among participants, 57.2% were female and 42.8% male. Most students (96.4%) agreed that computers aid learning, and 93.5% believed AI will be essential in future medical practice. Additionally, 77% felt AI would improve medical education, while 87.7% expressed willingness to receive AI-based instruction. A majority (89.1%) supported the inclusion of AI training in the curriculum, and 83.5% believed AI knowledge would benefit their careers. Female students were significantly more likely to support curriculum integration (p = 0.03) and AI instruction (p = 0.04), though no significant gender differences were observed in perceptions of AI’s future role (p = 0.12). Conclusion: Medical students exhibited highly positive attitudes toward AI. While they do not view AI as replacing physicians, they recognize its transformative role. Integrating AI education into MBBS curricula is therefore essential.

Objective: This study analyzes the effectiveness of the Education Office's high school Winter/Summer School camp during school vacations for enhancing the artificial intelligence (AI) digital competences of high school students aspiring to become future teachers, and students' satisfaction. Methods: To measure the effects and satisfaction, we conducted pre- and post-course surveys using Google Forms to assess students' perception of AI, AI literacy, and teaching efficacy. We supplemented these quantitative data with qualitative data by utilizing participants' learning logs and conducting interviews. Finally, we fostered interaction among the participants during the learning process through Naver Band, and observed the outputs and learning process through activities such as creating teaching and learning content using AI and digital tools. Results: We found high satisfaction levels, and significant positive effects on students' perception of AI, AI literacy, and teaching efficacy. Clearly, the camp courses helped participating students develop AI literacy, enhance teaching efficacy, and build their competencies as future teachers. Conclusions: The "AI and Digital Competency Education Practicum for Future Teachers" conducted in the Winter/Summer School can enhance participants' AI literacy, teaching efficacy, and overall learning satisfaction. This has important educational implications for future teacher training.

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

Artificial Intelligence (AI) in education sector is leading to a transformation in education. Whereas AI has a number of advantages like individualized learning and efficiency, it also poses some challenges involving ethics and human interaction. This paper will explore the potentials of AI applied to education, discuss its drawbacks, and review future trends that will shape the learning landscape. The article evaluated the pros and cons of AI in education, based on the views of fifty academics from different universities using a mixed method approach. The conclusions brought out of the analysis are in line with similar studies existing in the literature. For academics, the integration of AI has many positives aspects on the learning–teaching process including improvement in skills and competences of students. The findings reveal a strong correlation between participants' awareness of AI-related risks in education and their perceptions of AI's impact on the college education system, emphasizing the intricate link between awareness and attitudes towards AI in education. While the negative aspects brought out from the research are linked to generalizability. The contribution to be drawn from the results of this research is mainly empirical and practical. These opinions should be used as resources for managers, policy makers and researchers suggesting avenues for future research to broaden the scope and include diverse educational contexts

This study examines EFL teachers' perceptions of artificial intelligence (AI) integration in a public secondary school in Santa Elena, Ecuador, employing a sequential explanatory mixed-methods design. Phase 1 consists of a systematic scoping review (PRISMA-ScR) synthesizing 21 peer-reviewed studies published between 2020 and 2025 on AI-enhanced EFL teaching, with emphasis on resource-constrained and Latin American contexts. Phase 2 reports descriptive findings from the EFL Teachers' Perceptions of AI Integration Survey (ETPAIS), a researcher-designed 24-item Likert instrument administered to all eight EFL teachers at the Unidad Educativa Otto Arosemena Gómez (N = 8; census sampling). Results reveal that teachers hold positive attitudes toward AI as a complementary pedagogical tool (D2: M = 4.13) but perceive institutional infrastructure as substantially inadequate for implementation (D3: M = 2.31). Professional development emerged as the most urgently demanded facilitating condition (Item 22: M = 4.63). These findings converge with international evidence regarding positive teacher conceptualizations of AI while diverging significantly regarding infrastructure sufficiency, confirming that adoption strategies derived from well-resourced contexts are not directly transferable to Ecuadorian public secondary education. Implications for educational policy and teacher training are discussed.

This study investigated the knowledge and perception of artificial intelligence among library and information science professionals in Zambia by surveying 245 randomly selected participants. The results show that the library and information science professionals had a favourable and encouraging outlook with regard to artificial intelligence but were also concerned about artificial intelligence replacing librarians’ roles and the barriers to adopting artificial intelligence technologies in Zambian libraries. The article suggests that libraries should consider the study’s findings before implementing artificial intelligence, particularly concerning technology and facilities, librarians’ proficiency in artificial intelligence, and leadership positions in artificial intelligence initiatives. The research can be used as a resource by library boards and associations to develop policies for implementing artificial intelligence in academic libraries, and fills a research vacuum in developing nations like Zambia regarding the knowledge of university and college libraries, and their willingness to use artificial intelligence.

The rapid integration of artificial intelligence (AI) technologies into academic and professional practice has profoundly shaped the communication and public relations domain. However, there is limited empirical understanding of how individuals actually perceive and use these new technologies. Therefore, this article investigates Communication and Public Relations (PR) students’ perceptions of artificial intelligence (AI), specifically aiming to understand how literacy, psychological factors, and trust influence their AI word-of-mouth (WOM). We proposed a theoretical model, which we tested using a quantitative approach with SmartPLS analysis on data gathered from 402 online questionnaires administered to students across three major Romanian universities. The key findings indicate that Internet use fosters AI literacy, which subsequently enhances both AI self-efficacy and the perceived ease of AI use. Crucially, higher AI literacy leads to greater trust, promoting informed choices. While AI self-efficacy encourages appropriate reliance on the technology and positively impacts behavioral intention toward AI, higher trust surprisingly leads to lower privacy concerns. Conversely, heightened privacy concerns increase algorithm aversion, which in turn negatively impacts both behavioral intention and WOM communication about AI. We also confirm that a positive behavioral intention is a strong predictor of increased WOM communication. These findings have significant implications for academia, policymakers, and PR practitioners by highlighting the necessity of boosting AI literacy to mitigate aversion and foster responsible AI adoption among future communication professionals.

Recruiter's perception of artificial intelligence (AI)-based tools in recruitment

This study examined the perceptions of counselors-in-training (CIT) on artificial intelligence (AI) in counseling practice and training. 31 CIT responded to three open-ended questions about the benefits, drawbacks, and overall attitudes of integrating AI into counseling practice and education. Thematic analysis was used to analyze the responses. The findings showed that CIT perceive AI as a clinical support tool, especially for tasks such as diagnosis and treatment planning. Additionally, CIT voiced concerns of potential overreliance on AI, ethical risks, confidentiality concerns, and the possible loss of human connection. Furthermore, although several CIT expressed cautious optimism regarding AI's use in counselor education, some expressed skepticism. These findings have significant implications for counselor education programs considering the integration of AI, and future research should further explore AI’s impact on professional development within this field.