Abstract

This study aimed to assess teachers’ attitudes, perceived competence, and challenges in using AI in teaching secondary science, examining differences based on age, sex, highest educational attainment, years of teaching experience, number of relevant trainings, and specialization. The study was conducted in the different high schools in the District II of Ifugao, Philippines. A quantitative descriptive research design was employed. Data were collected using an adapted questionnaire and analyzed using frequencies and percentages, and mean. Key findings indicated that teachers held a very positive attitude toward AI in science education, recognizing its ability to enhance learning, personalize instruction, and support critical thinking. Teachers perceived competence was examined using three domains: general competence, access to resources, and professional development. Respondents rated themselves very high (very competent) in general competence, demonstrating confidence in understanding AI concepts, integrating AI with traditional teaching methods, and evaluating AI’s effectiveness. Access to AI tools and support was also rated very high, with teachers reporting sufficient availability of software, technical support, and instructional materials. Professional development was rated high; teachers actively sought AI-related learning and perceived training opportunities as available, but the actual receipt, quality, and relevance of training were limited. The most severe challenges identified included concerns about data privacy, reliability of AI-based assessments, ethical issues, institutional policy ambiguity, and keeping up with rapid technological developments. The study recommends an action plan to enhance teachers’ knowledge and understanding of AI and its applications in science education, strengthen skills for integrating AI into lesson planning, instruction, and assessment aligned with MELCs, and promote ethical, responsible, and secure use of AI in compliance with DepEd data privacy and child protection policies.

1. Introduction

Artificial intelligence (AI) is revolutionizing the educational landscape, providing innovative tools and methods that enhance learning experiences for both students and teachers. By integrating AI into classrooms, educators offer personalized learning paths that cater individual student-needs, bridging gaps in understanding and enabling learners to progress at their own pace ¹. This personalized approach not only increases student engagement but also empowers them to focus on areas requiring improvement, fostering a deeper comprehension of the subject matter.

Despite its potential, the implementation of AI in education particularly in the locale of the study presents several challenges that must be addressed to ensure its effective and ethical use. Key concerns include data privacy, equitable access to technology, and the potential for over-reliance on AI solutions. These challenges are particularly pronounced in the context of science education, where the readiness and attitudes of teachers play a crucial role in the successful integration of AI technologies.

The integration of AI into teaching and learning faces significant roadblocks. One major issue is the lack of adequate technological infrastructure, especially in underserved and rural areas, where access to high-speed internet and modern computing devices is limited ². This digital divide creates disparities in educational opportunities, leaving students and educators in marginalized communities at a disadvantage. Furthermore, many educators lack the necessary training and confidence to effectively utilize AI tools, resulting in under-utilization of their potential ³.

Moreover, AI has become a transformative force in various fields, including education. Its integration into teaching is reshaping traditional methods, enhancing both instructional strategies and learning experiences. Artificial intelligence offers significant potentials in education by automating repetitive tasks, personalizing learning experiences, and providing valuable data-driven insights to improve teaching outcomes.

Artificial intelligence is reshaping education by supporting personalized learning, streamlining administrative work, and offering data-based insights for both teachers and students. While it helps meet diverse learning needs, its use also introduces ethical and practical concerns. Nevertheless, AI adoption in education continues to influence the educational landscape.

Furthermore, AI has been increasingly used in a variety of fields (e.g., industry, finance, and education) to promote innovation and increase work efficiency ⁴. In education, AI is touted as a seemingly enormous tool, supporting or even replacing teachers’ work by automatically tracking students’ progress, assessing their performance, and providing personalized help ^{5, 6, 7}. It is also reported that even teachers rely on AI to make informed decisions on orchestrating teaching practices to better support student learning ⁸.

In the context of the Philippines, particularly in the locale of the study, the integration of AI in education faces unique challenges and opportunities. The Philippine education system is characterized by a diverse range of learning environments, from urban centers with access to advanced technology to rural areas where resources are limited. One primary issue is the lack of adequate technological infrastructure, particularly in underserved regions, where access to high-speed internet and modern devices is often lacking ^{1, 2}. This digital divide creates significant disparities in educational opportunities, leaving students and educators in marginalized communities at a disadvantage.

Moreover, many educators in the Philippines lack the necessary training and confidence to effectively utilize AI tools, resulting in underutilization of their potential ³. The insufficient professional development opportunities for teachers hinder their ability to integrate AI into their teaching practices, which is crucial for enhancing student learning outcomes.

Additionally, there is a notable gap in policy frameworks and regulations surrounding the use of AI in education within the Philippine context. The absence of clear guidelines raises ethical concerns regarding data privacy and security ². Without standardized approaches to AI implementation, schools often experience fragmented and inconsistent adoption practices. Financial constraints further exacerbate these challenges, making it difficult for many public schools to invest in the necessary infrastructure and software, thereby creating inequities in access and quality of education.

In the realms of science education, the effective integration of AI is contingent upon the readiness and preparedness of the teachers. Al Darayseh ⁹ emphasizes the critical role of science teachers as implementers of educational technology in the classroom. Their perceptions, willingness, and competence in utilizing AI tools are essential in determining the efficacy and success of this integration ^{11, 12}. As key stakeholders in the educational process, science teachers must navigate the challenges and opportunities that accompany the integration of AI, ensuring that its potential is maximized for all learners ¹³.

Addressing the challenges of AI integration in education requires comprehensive policy reforms, investments in technological infrastructure, and the upskilling of educators to ensure that AI's potential is maximized for all learners in the Philippines. The Department of Education (DepEd) actively supports the responsible and appropriate use of AI in educational settings. This support is reflected in professional development programs and seminars, where teachers are trained to utilize AI tools effectively to enhance instructional practices and streamline administrative tasks. During these seminars, educators engage with AI applications that simplify lesson planning, assessment, and other teaching-related processes, demonstrating how AI can serve as a practical aid in facilitating learning while ensuring alignment with DepEd guidelines and ethical standards.

2. Methodology

The study employed a quantitative descriptive-comparative research design to determine the secondary science teachers’ attitudes, competence, and challenges in using AI in instruction. Guided by Creswell’s framework, the design enabled the collection of structured questionnaire data for statistical analysis without manipulating variables, thereby providing an accurate picture of AI integration in science education. The research was conducted in District II, Province of Ifugao, involving 68 secondary science teachers from select public and private high schools in Aguinaldo, Alfonso Lista, and Mayoyao. The sample size was computed using the Raosoft calculator (95% confidence level, 5% margin of error). The respondents were chosen through random sampling to ensure relevance to the study objectives.

The research instrument was adapted from Alshorman ¹⁴, titled “The Readiness to Use AI in Teaching Science: Science Teachers’ Perspective.” The questionnaire measured teachers’ demographic profile, attitudes toward AI, perceived competence, access to resources and training, and challenges in implementation using Likert-scale items. The instrument demonstrated acceptable reliability with a Cronbach’s alpha of .78. Data gathering followed the formal administrative procedures, including permissions from school authorities, and responses were organized, tallied, and securely handled. Statistical analysis was performed using SPSS, ensuring systematic reporting and confidentiality of the data.

For data analysis, descriptive statistics such as frequency, percentage, and mean were used to summarize teachers’ profiles and levels of attitude, competence, and challenges in AI use. Ethical standards were strictly observed in compliance with RA 10173 (Data Privacy Act) to protect participants’ personal and sensitive information throughout the research process.

3. Results and Discussion

Table 1 presents the attitude of secondary science teachers toward the use of AI in instruction. The results indicate a very positive attitude, with a category mean of 3.96, reflecting strong acceptance of AI as a pedagogical tool. Teachers perceived AI as capable of enhancing the quality of science education (M = 3.97) and personalizing learning for students (M = 3.97). The respondents also agreed that AI can improve teaching effectiveness (M = 3.94) and support the development of students’ critical thinking and problem-solving skills in science (M = 3.94). These results show that teachers associate AI integration with improved instructional processes and learning outcomes.

The data indicate teachers’ readiness to adopt AI. Teachers reported willingness to experiment with AI in teaching practices (M = 3.96) and recognized that AI will become an essential component of future education (M = 3.96). This reflects both positive perception and intention to integrate AI into classroom activities, which supports the feasibility of implementation in secondary science instruction.

The item on the concern about AI replacing human elements in teaching obtained the highest mean (M = 3.99). Although interpreted as very positive, the result reflects teachers’ awareness of potential risks associated with AI use. The finding suggests that teachers support AI integration while maintaining the importance of the human role in instruction. The results indicate that teachers view AI as a complementary tool rather than a substitute for professional judgment and teacher–student interaction.

The foregoing results reflect an encouraging openness to innovation. In the same way, a qualitative study of elementary teachers in Monkayo, Davao de Oro (Philippines) found that while teachers generally recognized AI as instructional aid and beneficial for engagement and efficiency, they also expressed concerns—including ethical issues, student dependence on AI, limited training, and resource constraints ¹⁵. In addition, the study conducted by Al Darayseh ⁹ found that science teachers in Abu Dhabi generally accepted the use of AI in the classroom, with overall mean scores indicating high acceptance and positive attitudes toward AI applications. The results show a high acceptability of the use of AI in the classroom by science teachers, with positive correlations to self-efficacy, ease of use, expected benefits, attitudes, and behavioral intentions.

In contrast to the findings, the study conducted by Daskalaki et al. ¹⁶, a cross-national survey of educators, showed that many were optimistic about AI’s potential, but also expressed concerns related to critical thinking, ethical issues, and the need for professional development. This implies that there are underlying challenges and hesitations among educators even in the presence of generally positive attitudes. Also, the study of Devanadera ¹⁷ suggested cautious openness to AI among pre-service teachers, underscoring the need for teacher education programs to integrate AI-focused training and ethical discourse to reduce anxiety and enhance readiness for responsible AI integration.

Table 2 presents the general competence of secondary science teachers in using AI in instruction. The results indicate a very high level of competence, with a category mean of 3.51. Teachers reported strong understanding of basic AI concepts relevant to education (M = 3.66) and high confidence in using AI tools for teaching science (M = 3.50). The respondents also indicated the ability to learn newly introduced AI technologies for educational purposes (M = 3.51) and to evaluate the effectiveness of AI tools in enhancing science learning (M = 3.59). These findings indicate that teachers possess sufficient conceptual and practical capacity to apply AI in classroom instruction.

The data further show competence in communication and instructional integration. Teachers expressed comfort in explaining the benefits and limitations of AI to students (M = 3.76) and in integrating AI tools with traditional teaching methods (M = 3.74). These results suggest that teachers can align AI-supported instruction with existing pedagogical practices rather than treating AI as a separate instructional component. Such alignment supports meaningful technology integration in secondary science education.

The lowest mean was obtained for the ability to troubleshoot minor technical issues in the classroom (M = 2.84), interpreted as high but comparatively lower than the other indicators. This suggests that while teachers demonstrate strong competence in conceptual understanding and instructional application, additional support may be required in the technical maintenance and problem-solving aspects of AI use ¹. The results indicate that professional development may focus on strengthening teachers’ operational skills to sustain effective AI integration in science teaching.

Table 3 presents teachers’ competence in using AI in secondary science instruction in terms of access to resources. The results indicate a very high level, with a category mean of 3.30. Teachers reported access to adequate AI resources for teaching science (M = 3.68) and a variety of AI software and educational applications (M = 3.56). The respondents also indicated that the available AI resources are current and relevant (M = 3.38) and that they possess instructional materials to complement AI tools in science teaching (M = 3.41). These results indicate that resource availability supports the integration of AI into classroom instruction.

The data also reflect institutional support. Teachers agreed that schools provide necessary technical support for AI use (M = 3.35), indicating the presence of assistance mechanisms for implementation. These findings suggest that beyond individual competence, organizational structures contribute to teachers’ ability to utilize AI in instruction effectively.

Lower mean scores were observed for the provision of sufficient hardware (M = 2.75) and reliable internet connectivity (M = 2.97), both interpreted as high but relatively lower when compared to the other indicators. These results suggest that while software access and instructional materials are available, infrastructure-related components require further strengthening. The findings indicate that improving hardware availability and internet reliability may enhance sustained and equitable AI integration in secondary science classrooms.

Table 4 presents teachers’ competence in using AI in secondary science instruction in terms of required professional development. The results show a relatively high level of competence, with a category mean of 2.61. Teachers reported that ongoing professional development opportunities related to AI are available (M = 3.71) and they are encouraged by their schools to attend workshops and conferences on AI in education (M = 3.50). The respondents also indicated that professional development activities are tailored to their subject area and grade level (M = 3.40) and they actively seek learning opportunities related to AI (M = 3.74). These findings indicate institutional support and teacher initiative in pursuing AI-related professional growth.

However, several indicators reveal gaps between availability and actual experience. Teachers reported low levels of having received training on how to use AI in teaching (M = 1.47), satisfaction with the level of training for AI integration (M = 1.41), and the practicality of training for classroom application (M = 1.03). These results indicate that while professional development structures exist, the training delivered is limited in coverage and applicability to classroom practice.

The contrast between high ratings on opportunity and encouragement and low ratings on actual training experience suggests a misalignment between policy and implementation. The findings indicate the need for structured, hands-on, and context-specific AI professional development programs to strengthen teachers’ operational competence and ensure effective integration of AI into secondary science instruction.

The teachers’ competence (Table 2, Table 3, and Table 4) was examined through three domains: perceived competence, access to resources, and professional development/training. Teachers rated themselves very high in perceived competence (M = 3.51). They felt confident in understanding AI basics, integrating AI with traditional methods, and evaluating AI’s effectiveness in instruction. However, teacher’s ability to troubleshoot technical issues scored lower (M = 2.84), revealing a gap in technical proficiency. Correspondingly, a quantitative study examined teachers’ use of an AI-enabled system ¹⁸ found that teachers reported high levels of technological competence (M = 4.12), training and support (M = 3.92), and positive attitudes (M = 4.24), which corresponded to a high acceptance of AI (M = 4.12). In contrast, the study of Alkubaisi ¹⁹ revealed that while STEM teachers perceive themselves as possessing moderate technological knowledge of AI tools, they recognize the potential of AI in delivering personalized and adaptive learning experiences. The findings reinforce existing literature on the pedagogical affordances of AI in enhancing teaching practices and improving student learning outcomes. Moreover, the study underscores a critical need to strengthen teacher readiness for AI integration, highlighting the role of targeted professional development in equipping educators with the skills and confidence necessary to effectively leverage AI in STEM education.

Access to AI tools and support was very high (M = 3.30). Teachers reported good access to software, support, and instructional materials. However, hardware availability (M = 2.75) and internet reliability (M = 2.97) were weaker areas, potentially limiting real-time or continuous AI use.

This result triangulates the study conducted by Vesna ²⁰ highlighting limited technological infrastructure such as unreliable internet and lack of devices that significantly constrains AI use in education, especially in developing regions. One research report found that teachers overwhelmingly cited inadequate infrastructure like poor internet connectivity, unreliable electricity, and lack of AI-compatible devices as barriers to adopting it in the classrooms. The study of Kim and Wargo ²¹ found that many teachers pointed to the “lack of IT-based classrooms” as a significant barrier to implementing AI tools effectively. Without the necessary infrastructure, integrating AI into daily tasks remains impractical. In addition, the study of Florentino et al. ²² reported significant limitations in infrastructure, including low access to computers and unreliable internet connections as common barriers to effective use of AI in schools although educators were enthusiastic about AI’s pedagogical potential. These structural challenges undermine teachers’ ability to adopt AI tools consistently.

Professional development was rated high (M = 2.61). Teachers actively sought AI learning (M = 3.74) and perceived training opportunities as available, but actual receipt, quality, and relevance of training were rated low (M = 1.03–1.47). This highlights a disconnect between training availability and effectiveness, which may hinder skill development. While teachers believe they are pedagogically competent in using AI, their technical skills and professional training lag behind. Addressing this training gap is essential for sustainable implementation ²³. Additionally, “a lack of knowledge and expertise in using AI tools” further worsens their discomfort. Many teachers admitted they have “no training in AI,” making the prospect of relying on these tools daunting ^{1, 18}. Also, a study highlighted that the use of emerging technologies such as AI to provide professional development for teachers has made a significant contribution to teacher quality, but they still lack the guidance needed on curriculum development and opportunities for collaboration with expert teachers ²⁴. According to Roshan et al. ²⁵, the most significant barriers to AI integration were lack of training (60%) and insufficient resources (40%). These findings underscore the necessity for targeted, continuous professional development to improve educators' readiness and ability to effectively utilize AI tools in their teaching practices. However, despite its benefits, AI integration in teacher Professional Development presents challenges, including ethical concerns, biases in AI algorithms, and resistance to technological adoption among educators ^{1, 26}. This research emphasizes the importance of AI literacy programs that equip teachers with the knowledge to critically assess AI-driven recommendations and ethically apply AI in teaching practices ²⁷. The study also highlighted the need for continuous professional development initiatives that ensuring educators remain updated with emerging AI advancements in education ²⁸.

Table 5 presents the challenges encountered by secondary science teachers in using AI in instruction. The results indicate a severe level of challenges, with a category mean of 3.53. Teachers expressed strong concern regarding data privacy and security issues associated with classroom AI use (M = 4.00). High levels of concern were also reported for the lack of clarity in school policies on AI integration (M = 3.88), ethical implications of AI in education (M = 3.91), and the reliability of AI-based assessment in monitoring student progress (M = 3.96). These results indicate that governance, ethics, and assessment validity remain critical barriers to AI adoption in secondary science teaching.

Time constraints and technological change also emerged as major challenges. Teachers reported insufficient time during the school day to integrate AI effectively (M = 3.24) and the difficulty in keeping pace with rapid developments in AI technologies (M = 3.91). These findings indicate that instructional workload and continuous technological evolution place pressure on teachers’ capacity to implement AI in a sustained and informed manner.

A comparatively lower mean was observed for the perception that AI could widen the gap between groups of students (M = 1.79), interpreted as a moderate challenge. This suggests that equity concerns are present but less dominant than issues related to policy clarity, ethics, reliability, and capacity. The results indicate that effective AI integration requires strengthened institutional policy frameworks, ethical guidelines, technical support systems, and time allocation mechanisms to reduce implementation barriers in secondary science education.

These findings align with prior researches highlighting that teachers are hesitant to adopt AI tools without robust data protection, fairness, and transparency mechanisms ²⁹. Furthermore, lack of clear institutional policies and guidelines has been identified as a key barrier to effective AI use in classrooms ³⁰. Studies also emphasize that ethical risks, including algorithmic bias and privacy concerns, remain central obstacles to AI adoption despite teachers’ positive attitudes toward its pedagogical potential. The study of Marcos ³¹ found that ethical considerations, including data privacy, algorithmic transparency, and academic integrity, are persistent challenges for AI adoption in education. Thus, without clear ethical frameworks and sound institutional policies, AI integration may be inequitable or ineffective, supporting various findings about the severity of ethical, policy, and privacy concerns.

4. Conclusion

Secondary science teachers demonstrate a very positive attitude toward using AI in instruction, recognizing its potential to enhance teaching effectiveness, personalize learning, and develop students’ critical thinking and problem-solving skills. Teachers exhibit high general competence, particularly in understanding AI concepts, integrating AI with traditional methods, and communicating its benefits, although their ability to troubleshoot technical issues is comparatively lower. Access to AI resources, including software, instructional materials, and technical support, is generally very high, but hardware availability and reliable internet connectivity remain limited. Professional development opportunities are available, and teachers actively seek learning experiences; however, practical, hands-on training is insufficient, indicating a gap between policy encouragement and actual skill-building. Teachers also face severe challenges in AI integration, including concerns about data privacy, policy clarity, ethics, assessment reliability, time constraints, and keeping pace with rapid technological developments.

These findings suggest that while teachers are willing and largely capable of using AI, effective and sustainable integration requires strengthened infrastructure, targeted professional development, and clear institutional policies. Schools and education authorities should provide up-to-date hardware, reliable internet, and technical support, along with practical, context-specific training programs that equip teachers to implement AI effectively. Establishing clear policies and ethical guidelines will address data privacy, assessment reliability, and equity concerns, enabling teachers to leverage AI responsibly and maximize its impact on student learning and innovation in science education.

References