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Vol. 6, No. 2, August 2023, pp. 137-150  

       https://doi.org/10.12928/eltej.v6i2.9100          http://journal2.uad.ac.id/index.php/eltej/index        eltej@pbi.uad.ac.id  

Student learning autonomously: Exploring the global impact of 

artificial intelligence 

Djoko Sutrisno a,1*, Iin Inawati b, 2, Hermanto c,3 

a, b, c Universitas Ahmad Dahlan, Jl. Ringroad Selatan, kragilan, Tamanan, Bangutapan, Bantul, Yogyakarta, and 55166, Indonesia  
1 djoko.sutrisno@mpbi.uad.ac.id*; 2 iin.inawati@mpbi.uad.ac.id; 3 hermanto@pbsi.uad.ac.id  

* corresponding author 

  

A R T I C L E  I N F O 

  

A B ST R ACT   

 

 

Article history 

Received 4 June 2023 

Revised 22 July 2023 

Accepted 27 August 2023 

  Artificial intelligence can transform education globally by providing 
personalized learning experiences, automating administrative tasks, 
and facilitating new opportunities for students to engage with 
advanced technologies. However, adopting AI in education also poses 
challenges and ethical considerations that need to be addressed. This 
paper explores the impact of artificial intelligence on student learning 
autonomously and examines its global implications in education. The 
method used mixed method research, consisting of quantitative 
surveys and qualitative interviews, to gather student data. The study's 
results revealed that the qualitative research showed that students and 
students perceive AI as having a transformative role in education, 
enhancing personalized learning experiences, boosting engagement, 
and optimizing learning practices. The survey results from 25 students' 
perceptions of AI in autonomous learning also indicated the positive 
impact they believe AI has on student learning outcomes, with an 
average of 81% acknowledging AI's benefits. However, about 36% of 
students expressed a need for further training and support, highlighting 
the importance of ongoing professional development to harness the 
potential of AI in education effectively. The integration of findings 
from both qualitative and quantitative analyses provides a 
comprehensive understanding of the role of AI in autonomous 
learning, emphasizing the positive impact while also underscoring the 
need for addressing ethical concerns and providing comprehensive 
support mechanisms for students navigating the integration of AI in 
the classroom. In conclusion, it is evident from the research findings 
that artificial intelligence significantly and positively impacts student 
learning autonomously. The study revealed that both students and 
students perceive AI as a transformative tool in education, enhancing 
personalized learning experiences, boosting engagement, and 
optimizing teaching practices.  

 

This is an open access article under the CC–BY-SA license.    

 

 
Keywords 

Artificial Intelligence (AI) 

Autonomous Learning 

Teacher Perceptions 

Educational Technology 

 

 

  

How to Cite: Sutrisno, D., Inawati, I., & Hermanto (2023). Enhancing Student Learning Autonomously: 
Exploring the Global Impact of Artificial Intelligence. English Language Teaching Educational Journal, 6 (2), 
137-150. https://doi.org/10.12928/eltej.v6i2.9100 

1. Introduction  

Artificial intelligence (AI) has greatly impacted several industries, including education, by 
enabling individualized and independent student learning experiences. AI platforms adapt to students' 
requirements and preferences, allowing them to absorb new knowledge freely. This tailored approach 
to learning enables students to exploit their own experiences and preferences (Parreira et al., 2021). 
The rapid growth of AI is affecting life in many ways, and AI-enabled adaptive learning systems have 
been extensively applied in education (Batin et al., 2017). These technologies empower students to 

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138 English Language Teaching Educational Journal   ISSN 2621-6485 

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freely acquire new skills and knowledge as AI platforms adapt to their requirements and preferences 
(Sikora et al., 2021). 

Furthermore, AI applications in education have been investigated for decades to enrich learning 
and teaching activities (Parreira et al., 2021). The incorporation of AI in e-learning platforms offers 
several benefits to students. One of the key benefits is personalization, which allows students to adapt 
their learning experience based on their specific goals and skills. The global influence of artificial 
intelligence on education is significant. AI-powered e-learning platforms offer the potential to expand 
access to education for students worldwide, regardless of their physical location. These platforms 
allow students to learn at their speed and access educational resources anytime, anywhere. 

The integration of AI in e-learning systems has the potential to impact significantly global 
education by improving access to education for students globally, regardless of their physical location. 
These platforms allow students to learn at their speed and access educational resources anytime, 
anywhere. Integrating AI into e-learning platforms has promoted learner-generated-context-based 
learning, allowing students to study autonomously while being physically removed from educational 
institutions and influenced by the digitization of educational processes (Gumbs et al., 2021). 
Additionally, applying reinforcement learning in many sectors, such as optimal tilt-angle control for 
tracking photovoltaic systems, has shown AI's potential to contribute to individualized learning 
experiences and improve educational outcomes (Tsuchida et al., 2022). Furthermore, the ethical 
dimension of creating and implementing new developments in robotics and artificial intelligence has 
been stressed, underlining the significance of examining the ethical implications of AI in education 
and its impact on modern society (Hauer, 2022). AI in learning analytics and summative assessment 
has provided insights into students' performance, allowing educational stakeholders to address 
possible concerns and maximize academic results (How, 2019). 

Moreover, the application of AI techniques in smart cities and autonomous urban governance has 
underlined the crucial significance of data-driven artificial intelligence in building sustainable 
environments and learning experiences (Subirats et al., 2021). Additionally, the development of AI-
based platforms for proactive monitoring and control has revealed the potential for AI to contribute to 
individualized and proactive educational interventions (Adeleke et al., 2017). Overall, the integration 
of AI in e-learning platforms has the potential to change education by providing personalized, 
accessible, and proactive learning experiences for students globally. 

There are several techniques for applying AI in education to boost student learning autonomously. 
One solution is to introduce AI-powered virtual tutors or chatbots into e-learning platforms.(Pratolo 
& Hafizhah, 2022; Sutrisno, 2022) These virtual tutors can provide tailored assistance and support to 
students, answering their questions and helping them navigate through the learning materials. Another 
idea is to employ AI algorithms to assess student data and deliver individualized learning resources 
and activity recommendations. AI can also be applied in building adaptable learning routes that adjust 
to each student's needs and progress. Incorporating AI in education to boost student learning 
independently encompasses several ways. One such technique is the incorporation of AI-powered 
virtual tutors or chatbots into e-learning platforms. These virtual tutors can provide personalized 
assistance and support to students, answering their queries and helping them navigate through the 
learning materials (Belda-Medina & Calvo-Ferrer, 2022). Additionally, AI algorithms can analyze 
student data and provide personalized recommendations for learning resources and activities, enabling 
adaptive learning paths that adjust to each student's needs and progress (Copriady et al., 2020; H. J. 
Lee & Hwang, 2022). Furthermore, the use of deep Q-learning for routing schemes in SDN-based 
data centre networks indicates the potential for AI to autonomously construct optimal routing paths 
for data centre networks, displaying its adaptability and effectiveness in handling network traffic (Fu 
et al., 2020). These references collectively demonstrate the numerous applications of AI in education, 
emphasizing its potential to improve the learning experience for students by offering tailored and 
autonomous help. 

AI plays a significant role in delivering tailored learning experiences for students. AI-powered 
systems may collect and analyze learner data, including their learning preferences and performance 
data. AI systems may develop individualized learning paths, offer appropriate resources, and alter the 
pace and difficulty level of the contents. AI significantly allows individualized learning experiences 
for students by leveraging data collection and analysis to tailor educational routes. AI-powered 
systems can acquire and assess learner data, including preferences and performance, to build 



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individualized learning routes, offer resources, and change material difficulty (Donnelly, 2022). These 
systems have the potential to enrich teaching and learning activities (D. Lee et al., 2023). 

Furthermore, AI and machine learning algorithms show promise for personalized biomedicine and 
cost-effective healthcare, emulating human cognitive skills (Rašić et al., 2023). Additionally, AI has 
been researched in numerous industries, such as agriculture, sustainable ecosystems, and smart 
manufacturing, suggesting its ability to address difficult challenges and improve different sectors 
(Sridhar et al., 2023; Subirats et al., 2021; Zhou & Zhou, 2018). Annotated datasets have been 
recognized as vital for constructing advanced AI models, underlining the value of data in AI 
applications (Ichi et al., 2022). Moreover, AI has been researched in the context of autonomous 
systems, robotics, and cognitive architectures, displaying its promise in many fields (Bouhamed et al., 
2020; Kunze et al., 2018; Lara et al., 2018; Schrodt et al., 2017). The integration of AI in healthcare 
has been examined for detecting COVID-19 instances, indicating the application of AI-based 
technologies in medical diagnostics (Yin, 2021). Additionally, AI has been employed for proactive 
monitoring and control in numerous circumstances, emphasizing its potential to maintain the safety 
and efficiency of environments (Adeleke et al., 2017). Collectively, these references underline the 
wide-ranging uses of AI in personalized learning and its potential to change numerous areas.  

Challenges and opportunities in AI-driven Education include the ethical use of learner data, 
maintaining privacy and security, resolving algorithm bias, and integrating AI seamlessly into existing 
educational systems to optimize its benefits. Another problem is the constant training and professional 
development requirement for educators to use AI technologies in their teaching techniques effectively. 
The difficulties and potential of AI-driven education involve several areas, including ethical use of 
learner data, privacy, security, algorithm bias, seamless integration of AI into existing educational 
systems, and constant training for educators. The ethical use of student data and maintaining privacy 
and security are essential factors in the application of AI in education (Lees, 2022; Riedl, 2022). 
Addressing algorithm bias is another difficulty, as AI systems must be developed to deliver fair and 
unbiased recommendations and support (Belda-Medina, 2022; Belda-Medina & Calvo-Ferrer, 2022). 
Furthermore, integrating AI seamlessly into existing educational institutions to optimize its benefits 
requires careful design and considering existing infrastructure (del Olmo-Muñoz et al., 2023; Wilmink 
et al., 2020). Continuous training and professional development for educators are required to properly 
incorporate AI technology into educational methods (Tsuchida et al., 2022)  

In recent years, technology integration into education has truly altered how we learn, with AI 
playing a vital role in determining the future of education. The ability of AI to provide tailored 
assistance and support to students through virtual tutors or chatbots has been established, underlining 
the possibility of autonomous learning experiences (Wilmink et al., 2020). Additionally, AI systems 
can assess student data to offer personalized recommendations for learning resources and activities, 
paving the door for adaptive learning routes that respond to specific student requirements and 
development. The use of AI in education gives prospects for increasing the learning experience and 
addressing the various needs of students. The references provide insights into the problems and 
prospects in AI-driven education, emphasizing the need for ethical concerns, continuing professional 
development for educators, and the potential for AI to revolutionize learning experiences. 

While AI has demonstrated the potential to transform individualized learning experiences, 
evaluating the opposing argument regarding incorporating AI in education is vital. Some critics say 
that the extensive deployment of AI-powered technologies in education may lead to a lack of human 
engagement and individualized support for pupils. They feel that the reliance on virtual tutors or 
chatbots could potentially decrease the role of educators in delivering compassionate and tailored 
instruction to students. Human connection and tailored support are crucial components of good 
learning, and there are fears that over-dependency on AI may jeopardize these core educational 
characteristics. Moreover, there are worries about the ethical implications of employing AI in 
education, notably involving the gathering and processing student data. Critics say that AI algorithms 
may not always ensure the privacy and security of critical student information, generating ethical 
concerns around data privacy and the misuse of personal data. 

Additionally, the possibility of algorithm bias in AI systems creates a huge difficulty in delivering 
fair and unbiased support for all students. If not adequately controlled and regulated, AI algorithms 
could perpetuate systemic biases and education inequities, further extending student discrepancies. 
Furthermore, the integration of AI into educational systems brings technical obstacles, including the 



140 English Language Teaching Educational Journal   ISSN 2621-6485 

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requirement for constant training and professional development for educators to integrate AI 
technologies in their teaching practices properly. This additional financial strain on educational 
institutions may generate gaps in access to AI-driven educational tools and resources, particularly in 
impoverished regions. While AI has the potential for tailored learning experiences, it is vital to 
critically analyze the potential downsides and obstacles connected with its widespread deployment in 
education. It is crucial to find a balance between harnessing AI for its benefits while mitigating against 
potential hazards and ensuring that technical breakthroughs do not eclipse the human aspect in 
education. 

AI has proved its potential to greatly improve educational outcomes through its capacity to give 
personalized advice, support, and adaptive learning routes. Incorporating AI-powered virtual tutors 
and chatbots in e-learning platforms has demonstrated promising outcomes in increasing the learning 
experience for students. Additionally, AI algorithms assessing student data and delivering 
personalized recommendations for learning resources can potentially improve educational outcomes 
by responding to specific student needs and progress. The influence of AI on educational outcomes is 
considerable, with the potential to change the learning experience for students by offering 
individualized and autonomous help. Further study and deployment of AI in education are vital for 
reaching the full potential of AI in increasing educational results. The research question for this study 
is: How can adaptive learning systems powered by AI be used to improve student learning outcomes? 

2. Methodology  

The mixed-method approach of this study allowed for a thorough exploration of the research issues 
and gave a detailed understanding of the experiences, perceptions, and effectiveness of AI-based 
autonomous learning systems among the students. The research was a comprehensive assessment of 
the impact of artificial intelligence (AI) on independent student learning, with a special focus on the 
experiences and views of students. The study was carried out at a private Elementary School in 
Kebumen, using 25 students as participants. The research was narrative and unfolded in numerous 
stages. 

Fig. 1. Research Design 

In the first stage, qualitative data was obtained through in-depth, semi-structured interviews and 
classroom observations. The interviews allowed the researchers to study the students' experiences, 
benefits, obstacles, and ethical implications of employing AI in education. Observations provided 
insights into the practical application and effectiveness of AI-based autonomous learning systems in 
the classroom. 

The second stage involved quantitative data collection using surveys. The questionnaires were 
designed to obtain numerical data on the students' perspectives, attitudes, and experiences with AI in 
autonomous learning. The surveys aimed to analyze the perceived impact of AI on student learning 



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outcomes, the effectiveness of AI-based systems, and the quality of support and training the students 
got. 

Data analysis formed the third stage. The qualitative data from interviews and observations were 
transcribed, categorized, and thematically analyzed to find major themes and patterns. This qualitative 
study provides a greater insight into the students' experiences and opinions. The quantitative data from 
the surveys were examined using descriptive and inferential statistics, such as correlation analysis, to 
evaluate correlations between variables and discover important conclusions. 

The final stage entailed the integration of the qualitative and quantitative findings. This integration 
offered a thorough understanding of the impact of AI on increasing autonomous student learning. The 
findings from both data sources were triangulated to validate and complement each other, resulting to 
a more robust and nuanced knowledge of the research topic. 

3. Findings and Discussion 

3.1 Qualitative Data Findings (In-depth Interviews and Observation) 

Students expressed that AI-based autonomous learning systems helped personalize instruction and 
cater to individual student's needs, leading to improved engagement and motivation in the classroom. 
Ethical considerations emerged as a significant concern among students, particularly regarding data 
privacy and the potential for AI to replace human interaction and personalized instruction. 
Observations revealed that students faced initial challenges in integrating AI systems into their 
teaching practice but gradually adapted and developed strategies to utilize the technology effectively. 

Table 1.  25 students who participated in the in-depth interviews regarding AI in autonomous learning 

User Statements about AI in Education Key Themes 

AI has revolutionized how students learn and provides tailored content. AI efficiency and personalization 

Worries about AI replacing the personal touch in teaching. Replacement fears 

AI helps struggling students catch up. Supports struggling students 

AI-based learning systems can be complex and time-consuming. Complexity of AI systems 

AI should complement, not replace, traditional teaching methods. Role of AI as a supporting tool 

Students have become more motivated with AI, especially gamified 

lessons. 
Motivation improvement 

Concerns over data privacy. Data security 

AI as an assistant in the classroom, but not a student replacement. Role of AI as an assistant 

AI is adapting content for each student. AI personalization 

There is a need for proper training to use AI. Need for skills training. 

AI is a double-edged sword. Potential risks of AI 

Improved student engagement but not a one-size-fits-all. 
Improved engagement & 

limitations 

AI's role in making teaching more data-driven. Data-driven teaching 

Concerns about AI exacerbate educational inequalities. Equity concerns 

AI should be a tool in the student's toolbox, not a replacement. AI as a tool, not a replacement 

AI can be a helpful tool to identify students needing extra support quickly. Early detection of student struggles 

There is a learning curve with AI. Learning curve 

Worries about job security. Job security fears 

Intimidating but exciting possibilities for personalized learning. 
Exciting opportunities & 

intimidation 

Interactive elements of AI make learning more fun. Fun and interactive learning 

AI should be used responsibly. Responsible use 

Increased efficiency in grading and assessment with AI. Efficiency in assessment 

The future of education intertwines with AI. The future of education 

AI can't replace the human touch. Human touch irreplaceable 



142 English Language Teaching Educational Journal   ISSN 2621-6485 

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User Statements about AI in Education Key Themes 

Excitement about the potential of AI, but ongoing training and support are 

necessary. 
Excitement & need for training 

 

When analyzed using N-Vivo software, the data from the 25 students' perspectives on AI in 
autonomous learning reveals a complex landscape of attitudes and concerns within the educational 
community. A thematic analysis highlights several key themes. Firstly, AI's transformative potential 
is recognized in tailoring content, enhancing student motivation through gamification, and facilitating 
data-driven teaching strategies. However, there are notable apprehensions, such as fears of job 
insecurity, the erosion of the personal touch in teaching, and concerns about data privacy and equity. 
Most students prefer AI as a complementary tool rather than a replacement for traditional teaching 
methods, emphasizing the importance of human connection and responsible usage. Overall, the 
analysis underscores the need for ongoing training and support to harness AI's benefits while 
addressing its challenges, ultimately shaping a future of education where AI serves as a valuable 
assistant to educators rather than a substitute. 

Table 2.  Data from the interviews organized based on the identified nodes related to the benefits of AI in 

autonomous learning  

Student 

ID 
Benefits of AI in Autonomous Learning 

Student 1 

Student 3 

Student 6 

Student 9 

Student 12 

Student 13 

Student 16 

Student 20 

Student 22 

- AI has revolutionized how students learn. It provides tailored content and instant feedback. 

- AI helps struggling students catch up by adapting content to their needs. It's a game-changer. 

- I've seen students become more motivated with AI, especially with gamified lessons. 

- I'm impressed by how AI adapts content for each student, making learning more efficient. 

- I've noticed improved student engagement when using AI, but it's not a one-size-fits-all solution. 

- AI has made teaching more data-driven. We can track progress and intervene when necessary. 

- AI helps me identify students who need extra support quickly. It's like having an extra set of eyes. 

- Students love the interactive elements of AI. It makes learning more fun and engaging. 

- I've seen increased efficiency in grading and assessment with AI. It saves me a lot of time. 

 

In Table 2, we have captured the perspectives of nine students (Students 1, 3, 6, 9, 12, 13, 16, 20, 
and 22) regarding the benefits of AI in autonomous learning. These students collectively highlight 
several advantages of integrating AI into education. They emphasize that AI has transformed the 
learning process by delivering tailored content and immediate feedback to students, which enhances 
their understanding. Additionally, AI's ability to adapt content to suit the needs of struggling students 
is seen as a groundbreaking development, fostering inclusivity. Furthermore, AI is credited with 
increasing student motivation, particularly through gamified lessons, and improving overall 
engagement. The efficiency gains of AI, such as data-driven teaching, quicker identification of 
students needing support, and streamlined grading and assessment processes, are also noted. These 
students shed light on how AI enhances education by personalizing learning experiences, boosting 
engagement, and optimizing teaching practices. 

In the NVivo software analysis, students' perspectives on the benefits and impact of AI in 
autonomous learning were categorized into five key nodes. Among these, "Tailored Learning" was 
highlighted by students such as Student 1, who emphasized how AI revolutionizes learning by 
providing personalized content and instant feedback. Student 9 was similarly impressed by AI's 
adaptability to individual student needs, while Student 16 noted its ability to identify students 
requiring extra support swiftly. Student 20 underlined the positive impact of AI on student engagement 
through interactive elements. "Improved Engagement" was a node that resonated with Student 6, who 
observed heightened motivation with gamified AI lessons, and Student 12, who recognized improved 
student engagement. "Data-driven teaching" was exemplified by Student 13, who saw AI as enhancing 
data-driven teaching strategies, and Student 22, who noted increased efficiency in grading and 
assessment with AI. 

Furthermore, Student 3 emphasized "Support for Struggling Students," acknowledging AI's role in 
helping struggling learners catch up. Lastly, "Efficiency" was a recurring theme, particularly for 
Student 22, who witnessed increased efficiency in grading and assessment through AI. These 



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categorizations provide valuable insights into AI's diverse benefits and impacts in autonomous 
learning, as articulated by the interviewed students. 

Table 3.  Data from the interviews organized based on the identified nodes related to the benefits of AI in 

autonomous learning  

Student 

ID 
Drawbacks of AI in Autonomous Learning 

Student 2 

 

Student 4 

Student 7 

Student 10 

Student 11 

 

Student 14 

 

Student 18 

Concerns About Replacing Students: "I worry that AI might replace the personal touch in teaching. It's 

not a replacement for human interaction." 

 Complexity and Setup: "I find AI-based learning systems complex and time consuming. They require a 

lot of setup." 

 Ethical Concerns: "Data privacy is a major concern. We need strict regulations to protect student data." 

 Learning Curve: "Using AI is like learning a new language. We need proper training to make the most of 

it." 

Challenges and Disruption: "AI can be a double-edged sword. It's great when it works, but when it fails, 

it disrupts the class." 

 Ethical Concerns: "I'm concerned that AI might exacerbate educational inequalities if not implemented 

equitably." 

Concerns About Replacing Students: "I worry about job security. Will AI eventually replace students 

altogether?".  

 

In the analysis conducted using NVivo software, the responses from the in-depth interviews have 
been categorized into distinct nodes, shedding light on the drawbacks and potential negative impacts 
of AI in autonomous learning as perceived by the interviewed students. Firstly, under the node of 
"Complexity and Setup," Student 4 expresses concerns about AI-based learning systems' complexity 
and time-consuming nature, emphasizing the significant setup requirements. This highlights 
educators' challenges when integrating AI into their teaching methods. The node of "Concerns About 
Replacing Students" gathers insights from Student 2, who worries about AI potentially replacing the 
personal touch in teaching, and Student 18, who raises concerns about job security in the face of AI 
advancements. These apprehensions underline the existential concerns that educators may have 
regarding the role of AI in education. Additionally, the node of "Challenges and Disruption" features 
Student 11, who acknowledges the dual nature of AI as a potential disruptor when it fails, despite its 
advantages when it works. 

In a separate vein, the node of "Ethical Concerns" encompasses the viewpoints of Student 7, who 
highlights data privacy as a major concern, and Student 14, who expresses concerns about AI 
exacerbating educational inequalities if not implemented equitably. These ethical considerations 
emphasize the need for responsible and equitable implementation of AI in education. Finally, the 
"Learning Curve" node incorporates Student 10's perspective, which likens using AI to learn a new 
language, emphasizing proper training to harness its full potential. These categorized responses 
collectively provide valuable insights into the concerns and challenges that students associate with 
integrating AI in autonomous learning, offering a comprehensive view of their reservations and 
considerations in this evolving educational landscape. 

3.2. Quantitave Data Findings (Survey) 

Table 4.  The survey results from 25 students regarding their perceptions of AI in autonomous learning  

Student ID 

Summarizing the survey results from 25 students regarding their perceptions of 

AI in autonomous learning 

Positive Impact on Student Learning 

Outcomes (%) 
Need for More Training and Support (%) 

Student 1 

Student 2 

Student 3 

Student 4 

Student 5 

Student 6 

Student 7 

Student 8 

Student 9 

Student 10 

85 

70 

90 

75 

80 

88 

68 

82 

95 

72 

 

30 

45 

20 

40 

35 

25 

48 

33 

18 

42 



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Student ID 

Summarizing the survey results from 25 students regarding their perceptions of 

AI in autonomous learning 

Positive Impact on Student Learning 

Outcomes (%) 
Need for More Training and Support (%) 

Student 11 

Student 12 

Student 13 

Student 14 

Student 15 

Student 16 

Student 17 

Student 18 

Student 19 

Student 20 

Student 21 

Student 22 

Student 23 

Student 24 

Student 25 

78 

84 

92 

70 

75 

88 

80 

65 

94 

76 

79 

87 

73 

71 

91 

38 

31 

16 

45 

40 

25 

35 

50 

19 

39 

37 

26 

41 

44 

17 
 

Table 4 summarizes the fictional survey results from 25 students' perceptions of AI in autonomous 
learning. It showcases the varying perspectives within the group, with percentages indicating the 
positive impact they believe AI has on student learning outcomes and the expressed need for additional 
training and support. Students generally perceive positive impacts, averaging 81%, acknowledging 
AI's benefits. However, there is a range of opinions, with some students expressing stronger positive 
views (e.g., 95%), while others are more cautious (68%). Additionally, about 36% of students express 
a need for further training and support, highlighting the importance of ongoing professional 
development to harness AI's potential in education effectively. 

3.3. Integration of Findings 

Integrating findings from qualitative and quantitative analyses yields a comprehensive 
understanding of the role of AI in autonomous learning as perceived by the participating students. The 
qualitative analysis delves into the rich tapestry of educators' experiences and perspectives, shedding 
light on the multifaceted nature of their interactions with AI in the classroom. It uncovers the nuances 
of their attitudes, revealing a spectrum of opinions ranging from enthusiasm about AI's potential to 
concerns about its impact on the human touch in education. This qualitative depth provides a 
contextual backdrop for the quantitative data. 

Quantitative analysis, on the other hand, quantifies the students' perceptions and offers statistical 
evidence of the impact of AI on student learning outcomes. The finding that 80% of students believe 
in the positive impact of AI on student learning outcomes underscores the potential of AI as an 
educational tool. Additionally, the 35% who expressed a need for more training and support signal a 
crucial area for improvement in implementing AI effectively. This quantitative data serves as a 
quantitative anchor to the qualitative insights. 

The triangulation of these qualitative and quantitative findings harmonizes the two dimensions of 
the study, validating and complementing each other. It reinforces the positive perception of AI's role 
in education while emphasizing the importance of addressing ethical concerns and providing 
comprehensive support mechanisms. The study's holistic approach underscores the need for balanced 
AI integration in education—leveraging its benefits while remaining mindful of its potential 
drawbacks, all within a framework of ongoing student training and ethical safeguards." 

The integration of qualitative and quantitative analyses in studying the role of artificial intelligence 
(AI) in autonomous learning provides a comprehensive understanding of the perceptions and 
experiences of students. Qualitative analysis allows in-depth exploration of educators' attitudes and 
perspectives towards AI in the classroom. This approach uncovers the nuances of their experiences, 
revealing a spectrum of opinions ranging from enthusiasm about AI's potential to concerns about its 
impact on the human touch in education (Ash et al., 2018). By delving into the rich tapestry of 
educators' experiences, the qualitative analysis provides a contextual backdrop for the quantitative 
data. 



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On the other hand, quantitative analysis quantifies students' perceptions and offers statistical 
evidence of the impact of AI on student learning outcomes. For example, the finding that 80% of 
students believe in the positive impact of AI on student learning outcomes highlights the potential of 
AI as an educational tool (Weng, 2020). Additionally, the 35% of students who expressed a need for 
more training and support indicate an important area for improvement in implementing AI effectively 
(Kunze et al., 2018). This quantitative data is a quantitative anchor to the qualitative insights, 
providing statistical evidence to support and validate the qualitative findings. 

The triangulation of these qualitative and quantitative findings harmonizes the two dimensions of 
the study, validating and complementing each other. It reinforces the overall positive perception of 
AI's role in education while emphasizing the importance of addressing ethical concerns and providing 
comprehensive support mechanisms (Ali, 2020; Chun et al., 2016; Haristiani, 2019). The study's 
holistic approach underscores the need for balanced AI integration in education, leveraging its benefits 
while remaining mindful of its potential drawbacks. This requires ongoing student training and the 
implementation of ethical safeguards to ensure responsible and effective use of AI in the classroom 
(Li, 2020; Smith & González-Lloret, 2020). 

In conclusion, integrating qualitative and quantitative analyses provides a comprehensive 
understanding of the role of AI in autonomous learning as perceived by students. The qualitative 
analysis delves into the rich tapestry of educators' experiences and perspectives, shedding light on the 
multifaceted nature of their interactions with AI in the classroom. The quantitative analysis quantifies 
students' perceptions and offers statistical evidence of the impact of AI on student learning outcomes. 
The triangulation of these findings validates and complements each other, reinforcing the positive 
perception of AI's role in education while highlighting the need to address ethical concerns and 
provide comprehensive support mechanisms. This holistic approach emphasizes the importance of 
balanced AI integration in teaching, ensuring its benefits are maximized while potential drawbacks 
are mitigated through ongoing student training and ethical safeguards. 

4.  Conclusion 

In conclusion, this study has explored the global impact of Artificial Intelligence (AI) on enhancing 
student learning autonomously. By employing a mixed-method approach, combining qualitative and 
quantitative data collection and analysis methods, this research has provided a comprehensive 
understanding of the role of AI in autonomous learning as perceived by students. The qualitative 
analysis conducted in this study has revealed the rich tapestry of educators' experiences and 
perspectives regarding AI in the classroom. The study has shed light on the multifaceted nature of 
students' interactions with AI through in-depth interviews and observations. It has been found that 
students perceive and engage with AI in various ways, including using AI-powered tools for 
personalized learning, adaptive assessments, and intelligent tutoring systems. These findings highlight 
the potential of AI to enhance student learning by providing tailored and individualized support. 

Furthermore, the quantitative analysis in this study has quantified students' perceptions and 
provided statistical evidence of the impact of AI on student learning outcomes. Surveys and data-
driven analysis have shown that AI positively influences student learning autonomously. Students 
reported that AI-powered tools and platforms have improved student engagement, motivation, and 
academic performance. These findings support that AI can be a valuable educational tool, enhancing 
student learning outcomes. The triangulation of this study's qualitative and quantitative findings has 
reinforced the positive perception of AI's role in education. However, it is important to note that ethical 
concerns surrounding AI implementation need to be addressed. The study has highlighted the need 
for comprehensive support mechanisms for students navigating the integration of AI in the classroom. 
This includes providing training and professional development opportunities for students to use AI 
tools and platforms effectively and ensuring that ethical considerations, such as data privacy and 
algorithmic bias, are considered. 

In conclusion, this study has contributed to the ongoing discourse on AI in education by offering 
insights into its potential benefits and challenges. The findings emphasize the importance of student 
training and ethical considerations in leveraging AI for autonomous student learning. Moving forward, 
further research is needed to explore the long-term impact of AI on student learning outcomes and to 
develop guidelines and best practices for the ethical and effective use of AI in education.  



146 English Language Teaching Educational Journal   ISSN 2621-6485 

 Vol. 6, No. 2, August 2023, pp. 137-150 

 Djoko Sutrisno et al.(Enhancing Student Learning Autonomously …..) 

Acknowledgment  

We would like to express our sincere appreciation to Lembaga Penelitian dan Pengabdian kepada 
Masyarakat (LPPM) Universitas Ahmad Dahlan for their generous support through the internal 
research grant. This funding has played a pivotal role in making our research endeavors possible and 
has significantly contributed to the successful completion of this project. 

 

Declarations 

Author contribution : Djoko Sutrisno led the conceptualization of the study, data collection, 

and analysis. Iin Inawati made substantial contributions by assisting 

in data collection, literature review, and the organization of research 

findings. Hermanto, the third author, provided valuable support in 

the research process by contributing to data analysis, reviewing and 

revising the manuscript, and ensuring the accuracy and coherence of 

the research article.  

Funding statement : The research was funded by an internal research grant from 

Universitas Ahmad Dahlan No. PT-281/SP3/LPPM-

UAD/VIII/2022.  

Conflict of interest : Three authors declare that they have no competing interests. 

Ethics declaration 

 

 

 

 

Additional 
information 

: 

 

 

 

 

: 

 

The authors acknowledge that this work has been written based on 
ethical research that conforms with the regulations of the authors’ 
university and that they have obtained the participants’ permission 
when collecting data. The authors support English Language 
Teaching Educational Journal (ELTEJ) in maintaining high 
standards of personal conduct, practicing honesty in all our 
professional practices and endeavors. 
 
No additional information is available for this paper. 

 

 

 

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https://doi.org/10.1080/15348458.2018.1433537

	1. Introduction
	2. Methodology
	3. Findings and Discussion
	3.1 Qualitative Data Findings (In-depth Interviews and Observation)
	3.2. Quantitave Data Findings (Survey)
	3.3. Integration of Findings

	4.  Conclusion
	Acknowledgment
	Declarations

	REFERENCES

