www.ijcrt.

org © 2024 IJCRT | Volume 12, Issue 2 February 2024 | ISSN: 2320-2882

AI Tools In Education: Perspectives From

Learners And Educator

Ekta Sharma, Vicky Mehta, Dr. Anurag Pahuja

Student, Student, Professor

Mittal School of Business

Lovely Professional University, Phagwara, India

Abstract:This study evaluates the use of artificial intelligence and its impact on student learning and perform
ance. The aim is to determine the impact on learning and examine the impact of intellectual property on teac
hing and practice. This study provides both quantitative and qualitative results. A survey on learning factors
was developed for the study population and used by expert decision makers. Surveys and the Google Forms
platform were used to collect data. The Kaiser-Meyer-Olkin goodness-of-fit test and Bartlett's standard
deviation test was used to evaluate the separation of data in this study. Rank score was calculated to evaluate
differences between variables. The Kaiser formula and Scree test should be reviewed to determine which
points are acceptable. Varimax orthogonal factor rotation method was used to reduce the number of variables
carrying high loadings on each factor. The results show that the analysis not only controls irrelevant items but
also provides important implications for the research data. Applying analytical methods to evaluation
problems can offer important insights to decision makers and help them focus on a few important things rather
than many. This article discusses students' and teachers' perspectives on the use of these skills.

Index Terms - artificial intelligence, academic performance, education, factor analysis, transparency,
qualitative study

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I. INTRODUCTION

The rise of Artificial Intelligence (AI) represents a profound transformation across various sectors, with a
significant impact on education. AI tools integrated into educational settings have the potential to
revolutionize learning experiences for both educators and learners. This study examines the differing
perspectives of educators and learners concerning the implementation and utilization of AI tools in the
educational sector.
AI tools in the educational sector refer to the application of artificial intelligence technologies within
learning environments to enhance teaching methods, personalize learning experiences and optimize
educational outcomes. These tools encircle a wide range of AI applications and systems that assist educators
and students in various aspects of the learning process.
 Personalized Learning: AI tools can comply to individual learning styles and paces, offering
personalized educational content and pathways for students. These tools can analyze student
performance and behavior to provide tailored materials and learning experiences, catering to the specific
needs of each learner.
 Adaptive Learning Systems: AI algorithms are used in these systems to assess student’s strengths and
weaknesses. They adjust the curriculum or learning materials in real-time based on students' progress,
ensuring a more tailored learning experience.
 Virtual Tutors and Chatbots: AI-powered virtual tutors or chatbots are available to students, offering
support, answering questions, and providing explanations in real-time. These tools can assist in
reinforcing learning materials, offering immediate help to students when educators might not be
available.
 Automated Grading Systems: AI-driven grading systems are capable enough to efficiently evaluate
assignments, quizzes, and exams, providing quicker feedback to students. This technology can save
educator’s time on grading, allowing them to focus more on teaching and mentoring students.
 Language Learning and Translation Tools: Language learning applications powered by AI and
translation tools facilitate language acquisition by offering interactive lessons and real-time translation
services, breaking down language barriers in educational settings.
These tools conform to individual learning styles and paces, offering personalized educational content and
pathways for students. They analyse student performance and behaviour to provide tailored materials
and learning experiences, meeting the specific needs of each learner.
Adaptive Learning Systems utilize AI algorithms to assess students' strengths and weaknesses, adjusting
the curriculum or learning materials in real-time based on their progress, thereby ensuring a tailored
learning experience. Virtual Tutors and Chatbots powered by AI offer real-time support, answering
questions, and providing explanations to students, reinforcing learning materials and offering immediate
assistance when needed.
AI-driven grading systems efficiently evaluate assignments, quizzes, and exams, providing prompt
feedback to students, thereby saving educators time on grading, and allowing them to focus more on
teaching and mentoring. Data Analysis and Predictive Analytics enable educators to identify trends, predict
student performance, and assess teaching methods' effectiveness, adapting strategies and providing targeted
support to students accordingly.
AI also contributes to the creation and curation of educational content, generating lesson plans, designing
instructional materials, and recommending resources, thereby fostering the development of high-quality
educational resources. Additionally, AI-powered language learning applications and translation tools
facilitate language acquisition, breaking down language barriers in educational settings.
The rapid integration of AI tools in education has generated considerable interest and discussion among
educators and learners worldwide. While these tools offer personalized learning experiences and tailored
instructions, their widespread integration prompts critical examination of ethical considerations and
challenges.
Educators' perspectives revolve around how AI tools can enhance teaching, streamline administrative tasks,
personalize learning, and support students, while learners focus on the immediate impact on their learning
experiences, including personalization, accessibility, and ease of use. However, concerns exist regarding
the displacement of traditional teaching methods, ethical implications, overreliance on AI, and the loss of
human interaction.

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Understanding both educators' and learners' perspectives is crucial for navigating the integration of AI tools
in education and realizing their potential to enhance student learning outcomes. This dynamic interplay
between technology and education highlights the evolving relationship between technology and the
educational landscape and its potential impact on the broader education system.
Need and Scope of the study
The need for this study arises from several critical gaps and limitations in the existing body of literature
related to the subject matter. The existing studies predominantly rely on case-based and conceptual
approaches, which do not provide the depth of insights that empirical research based on primary data
collection can offer. The main purpose of this study is to determine the main factors which are affecting
both the leaners and educators experience towards artificial intelligence tools.
II. Review of Literature

Sajja et al (2023) Their focus is on developing and implementing the AIIA system, that uses advanced AI
and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform in
higher education. The study acknowledges the challenges and limitations encountered during development.
These challenges can be valuable learning experiences and should be addressed in future iterations to improve
the system's effectiveness and reliability. Guan & Mou (2020) Their main aim of the critical research is to
identify historical events, key research topics, and changes related to research on AI-induced instructional
change from 2000 to 2019 and to present the emergence of student analysis patterns and learning analytics in
educational research. This study contributes to the AI literature by identifying important research shifts in the
use of AI and deep learning in education. It helps position ongoing AIEd research and initiates dialogue about
emerging research themes for the next decade. David Sander s, Gegov (2013) In his paper, he reviewed seven
AI tools useful in collaborative automation: cognitive processes, fuzzy logic, automatic recognition, neural
networks, genetic algorithms, case-based reasoning, and ambient intelligence. The tools and techniques
described in this article are less complex and can be used on small assembly lines, single robots, or machines
with low-end microcontrollers. Kavitha & Lohani (2019) their paper focused on discussing the diverse uses
of e-Learning, including employee training, skill development, self-directed learning, and the need for a
suitable learning management system (LMS). The article also highlights the integration of AI to improve the
eLearning experience. The key implication is that e-Learning research needs to continue gathering robust
evidence to validate its effectiveness and that AI can enhance eLearning but is not a complete substitute for
instructor-based learning, highlighting the importance of maintaining a balance between technology and
human guidance in education. Hosseini et al (2023) Their study focused to inscribe the ethical issues related
to the use and disclosure of AI tools like ChatGPT and LLMs in scholarly manuscript creation. It explores the
appropriate recognition, citation, and disclosure practices for LLMs and argues against considering them as
authors due to their lack of free will. Scholarly manuscript creation using Large Language Models (LLMs)
raises complex ethical challenges that cannot be effectively addressed by banning them. Instead, policies
promoting transparency and accountability, through disclosure in the introduction or methods section, in-text
citations, and supplementary materials, are recommended to ensure proper recognition while not attributing
authorship or responsibility to LLMs. BaidooAnu & Ansah(2023) Their research focused on the aftermath
of ChatGPT, a generative Artificial Intelligence tool, on education and how it can potentially revolutionize
teaching and learning. The study explores the benefits, drawbacks, and recommendations for leveraging
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ChatGPT in the educational context.The key implications are that ChatGPT and similar generative AI models
have the potential to revolutionize the educational landscape by offering personalized and interactive learning
experiences, improving the efficiency of tasks such as essay grading and language translation, and enhancing.
Holmes et al (2023) in their paper focused from automating traditional teaching methods to exploring AI’s
potential in fostering collaborative, learning, enhancing assessment methods and assisting teachers. The
application of AI in education may unintentionally amplify biases and reinforce existing assumptions, while
intelligent tutoring systems face challenges in balancing personalized learning and collective effectiveness,
potentially overlooking individual nuances. Roll & Wylie (2016) in their study talked about the evolutionary
process focuses on collaborating with teachers, diversifying technologies and domains, and improving current
classroom practices in AIED research. The revolutionary process aims to embed AI technologies in students'
everyday lives, supporting their cultures, practices, goals, and communities, bringing a transformative impact
on education. AIED researchers should continue their work, taking bolder steps, and integrating ILEs with
various learning environments, cultural norms, and learners' daily lives. The metaphor of a human tutor may
no longer suffice, and the aim should be to create mentorlike systems that go beyond domain knowledge to
support lifelong skills and peer interaction. Kizilcec (2023) in his study the focus is on the adoption and impact
of Predictive Learning Analytics (PLA) in education, where machine learning and AI are used to provide
insights and risk predictions to educators, ultimately aiming to enhance student success and retention in both
K12 and higher education institutions. Technology designers must focus on understanding educators' needs,
workflow, and resources to create AI tools that align with their daily practice. Attention to usability, reliability,
and integration into common systems can enhance AI adoption in education. Future research should continue
exploring these factors to optimize educators' experiences with AI systems. Akgun (2021) in his research the
major focus is on (1) defining AI through concepts like machine learning and algorithms, (2) introducing AI
applications in education, emphasizing benefits for students' learning, (3) addressing ethical challenges and
dilemmas of AI in education, and (4) providing recommended instructional resources to help teachers and K-
12 students understand AI and its ethical implications. The paper provides valuable insights into the
possibilities and ethical concerns surrounding AI integration in education. The highlighted instructional
strategies and resources offer the potential to assist both students and teachers in harnessing AI's benefits while
addressing privacy concerns and bias. Božić (2023) in his research the major focus is on the integration of
digital tools into teaching methods, leveraging modern technology to create an improved learning environment
for primary school students. Further research is needed to effectively integrate Ai based tools into primary
school instruction, evaluate long term effectiveness, and address ethical and equity concerns. AI can empower
teachers with valuable insights if designed and implemented thoughtfully. Khoroshavin b et al (2018) their
study revolves around exploring the transformative influence of Artificial Intelligence (AI) in education,
particularly through personalized educational content, pioneering teaching techniques, technology-driven
assessment methods, and the evolving dynamics of student-lecturer communication, aiming to predict and
understand the future landscape of education shaped by AI integration. The study underscores the evolving
role of AI in education, emphasizing its integration into diverse educational facets like assessment,

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personalized learning, and content development, but highlights the necessity of preserving human interaction
and mentorship within the learning process. Pinzolits (2023) In his research he talked about the exploring the
efficacy of AIbased NLP tools for optimizing academic research processes and enhancing educational
outcomes.AI-based tools offer immense potential to enhance research efficiency and academic writing but
raise critical ethical and authenticity concerns, requiring continuous evaluation and ethical integration into
academic practices for sustainable advancement. Karki & Karki (2023) in their research they talked about
the AI's impact on education, this study delves into the evolving role of AI in transforming educational
structures while emphasizing the need for a symbiotic relationship between AI and educators to enrich rather
than replace traditional teaching methodologies. The study highlights the need for a balanced integration of
AI in education, emphasizing ethical considerations, educator roles, and the ever evolving landscape while
acknowledging the necessity for further exploration into emotional learning aspects and cross-cultural
disparities in AI adoption within academia. Liang (2023) in his paper he is addressing the impact of AI tools
in education by emphasizing creativity-centered assessment methods and implementing AI literacy programs
to preserve humanities' value amidst technological advancements.It includes the necessity for redefining
testing methods in humanities education and integrating AI literacy courses to address AI tool usage, ensuring
the humanities adapt and leverage AI advancements while mitigating potential drawbacks. Nguyen (2023) his
study focus is to analyze and categorize past AI applications in education to create a framework for the
development of new AI approaches, considering their benefits, potential drawbacks, and ethical
considerations. The key implications involve addressing ethical concerns, technical limitations, and cost issues
while categorizing AI applications, ensuring the development of safe and effective AI solutions for education.
Chan (2023) in his research the major focus is on exploring the prospects of AI in higher education, assessing
whether it will replace or assist human teachers, with findings emphasizing the irreplaceable qualities of
human teachers while proposing strategies for incorporating AI to enhance learning and teaching. Emphasize
the symbiotic relationship between human teachers and AI in education, integrating AI to support educators
while preserving human qualities essential for student growth and advocating for ethical AI development.
Saylam et al (2023) in their research the focus is on addressing ethical challenges arising from students' misuse
of AI in education, proposing guidelines to optimize AI's beneficial use, and fostering an environment that
supports students and faculty in their academic endeavours. The ethical use of AI in education is crucial
therefore, educating students for leveraging its benefits, necessitating a balance between progressive teaching
methods and ethical considerations for maximized educational advantages. Rayhan & Rayhan (2023) their
primary research focus of the paper centers on exploring AI's transformative potential in personalized learning
experiences, ethical considerations, and safeguarding student data privacy within the educational landscape.
Responsible implementation and continued collaboration will be vital in fully harnessing the prospects of AI
to shape the posterity of education while ensuring ethical considerations and overcoming challenges. Hwang
(2020) their major research focus revolves around defining AIED's roles, evaluating its impact on students,
and exploring innovative AI supported learning strategies, while addressing ethical considerations and the use
of big data in educational contexts. Artificial intelligence in education has the potential to transform teaching

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and learning by providing opportunities for teachers and students. The publication of the journal highlights
the importance of artificial intelligence in education and the need for rigorous research in this emerging field.
Chen et al (2020) their major research focus is on the trends in AIEd research, the advocacy of AI technologies
in education, and the need to incorporate advanced techniques, deep learning, and educational theories to
enhance educational outcomes. They are practical guidance for newcomers, potential for international
collaborations, and enhanced understanding of AIEd trends. Ouyang & Jiao (2021) their major research focus
revolves around how AI techniques are applied to enhance educational practices and empower learners,
moving towards learner agency, personalization, and data-driven, personalized learning. The research
underscores the need for developing learner-centered, data-driven, and personalized learning in the knowledge
age. It suggests that AIEd should go beyond technology implementation and align with educational theories.
Aker & Mbiti (2019) in their study they highlights of the research include analyzing the potential of artificial
intelligence to improve equity and performance in education, learning how governments and schools can adapt
programs inherent in education to ensure the integration of intelligence, and solving problems and policies
related to intelligence in education. Intelligence in Education The future development of education is aimed
at sustainable development. This is to help countries and their schools begin to understand more information
to provide more personalized education. Chen et al (2020) Their study focus is on how AI has been adopted
in educational institutions, its evolution from computer-based technologies to web-based systems and
humanoid robots, and the effects on administration, instruction, and learning, including personalized
curriculum and improved teaching quality. Key implications are enhanced teacher effectiveness, improved
learning experiences, and the transformation of education. Renz et al (2020) their major research focus
includes an overview of their current status, exploring the relationship between the two, and investigating the
lack of real applications for AI in education in the European market, examining datadriven business models
and their connection to market growth. The article highlights a gap between theoretical understanding and
practical relevance of AI in education. Key implications are the need to focus on practical implementation,
explore regional differences in AI adoption, and address data security and ethical concerns. Chen et al (2021)
His main research interests are on various artificial intelligence technologies, including artificial intelligence,
language processing, robotics, data mining, speech analysis, neural networks, inference and consensus, and
their applications in educational settings.The article also addresses the challenges and future directions in
AIEd. Key implications are the need for transparent learner data usage, involving instructors in AI system
design, and transitioning to "DLEd" for educational system design. Liu (2021) his major research focus
includes exploring AI's impact on teaching and learning, discussing its potential benefits for teachers and
students, and addressing future challenges in AI's integration into education for promoting reform. Key
implications include the need for better AI integration, personalized learning, and teacher-student adaptation.
Devedžić (2021) his major research focus is on exploring the potential of key WI components, including
intelligent Web services, semantic markup, and Web mining, to address new and challenging research
problems in AIED. The paper briefly mentions ontologies, adaptivity, personalization, and agents as well. Key
implications include the promise of improved course sequencing and content presentation, automation of

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various educational activities, and more structured educational material organization. Baker his major
research focus includes the evolution of theories and models for collaborative learning, integrating educational
technology into schools, and using models as the basis for designing educational technologies. The article
emphasizes the importance of AIED research addressing all three roles of models in education. The key
implication is the need to "open up" the curriculum, educational technologies, and educators themselves to
adapt to educational technology, which involves complex challenges on structural and institutional levels.

Study Objectives:

 To identify the important factors that are affecting the learning experience.
 To investigate the implications of AI tools on pedagogy and educational practices.

Methodology:

Research Design:
The scientific paradigm is a lens that changes and influences the way we view the world. Information is
perceived or interpreted. Scientific philosophy is a belief and way of thinking about the development of
knowledge. Ontology, epistemology, and axiology follow these ideas from two perspectives: objectivism
and subjectivism. Additionally, the selection of appropriate studies depends on the context and nature of
the study. The research was designed as descriptive, and a survey distributed to students studying at various
universities was used. To understand the specificity and depth of teaching practice, qualitative research is
considered the most appropriate method because it provides a deeper understanding rather than a general,
specific understanding, especially when the nature of the research is exploratory.
Sampling Size and Techniques:
Learner: For the quantitative approach, a fully structured questionnaire can be a great use for investigating.
In case of factor analysis, a comprehensive interpretation can be done from 100 sample size. In this research
data is collected a total of one hundred and eighty-seven participants. The participants were selected using
convenience sampling, with accessibility and availability being the key drivers of sample selection.
Educator: The minimum acceptable standard for conversation quality is 1, which is beneficial for cognitive
development. Qualitative researchers should reference saturation and population homogeneity when
justifying the sample used. When participants belong to a group, a small sample size is sufficient and data
saturation can be achieved after 6 in-depth interviews. In case of Phenomenological Analysis (IPA), a
general description of the phenomenon can be made on a small sample, so it is recommended to use 6
samples for this type of study. In this study, an in-depth interview was conducted with all twenty
participants in the teaching practice. This sample size is sufficient to obtain positive findings in this study
and follow similar studies in the past. Participants were selected through convenience sampling;
accessibility and usability were the main driver of sample selection.

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Data Collection
Learners: For this research, the researcher made the fully structured questionnaire which is a quantitative
method to investigate the factors which are affecting the learning experience. The researchers used google
forms for collecting the data from the participants.
Educators: This study requires an in-depth interview, which is a good way to investigate the impact of
intellectual property on education. The researchers visited teachers in various areas and conducted
interviews with those who consented to participate in the study to obtain a deeper grasp of the work.
Researchers interviewed 20 participants ranging in age from 30 to 50. All interviews were audio recorded
and transcribed. It's easy to edit or repeat questions. These participants are not opposed to the publication
of research data.
Tools used:
For this study we used techniques and tools for methodologies: SPSS to create the factor analysis, Google
forms for data collection.

Analysis & Key findings

Cronbach’s Alpha:
Using Cronbach's alpha, the questionnaire's reliability was evaluated. It offers a simple method for
determining the score's reliability. Many items are believed to assess the same underlying construct. For
instance, separate questions in a student survey may ask different things, but the combined answers are
assumed to indicate overall satisfaction. Consistency is gauged by Cronbach's alpha.

Factor Analysis:
This study used analytical methods to analyse the data set to identify relationships between elements and
groups of elements that are part of a composite concept. The approach does not distinguish between
freedom and progress due to its investigative nature. A factor analysis just looks at the profile correlation
matrix to determine the underlying variables, grouping variables into the same factor. This study used
factor analysis with outliers to examine whether courses were related to the learning experience.

KMO and Bartlett’s Test:

Table 1 The sample size is adequate, and the index is appropriate for the data, as demonstrated by the KMO
value index of 0.877>0.6. The sphericity of the correlation matrix was assessed via Bartlett's test. The
results of Bartlett's test of sphericity show that the correlation matrix is significantly correlated on at least
some variables, with a p-value of less than 0.001. In this instance, the correlation coefficient is less than
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0.0001 and the test value is 765.347. As a result, it is not true that the correlation matrix is an identity
matrix. Specifically, the transformations are not perpendicular. When the significance value is less than
0.05, it means that the data set can be analysed.

Table1: KMO and Bartlett’s test

Factors Extraction:
Use the Kaiser test and Scree test to determine whether the threshold number changes from negative. The
eigenvalues associated with each factor represent the rate of change defined by a specific linear
component. Coefficient values below 0.4 are suppressed, which suppresses the presentation of significant
loads with a value less than 0.4.

Table2: Kaiser’s Criteria

Table 2 provides an explanation of the overall transformation and the eigenvalues. Fundamental analysis
was the search and extraction strategy employed in this investigation. Prior to extraction, eleven linear
sequences were found in the data set. Two distinct rows with eigenvalues greater than one can be found in
the data after subtraction and rotation. Together, the two subtractions account 45.991% of the variation in
total. A graph for the Scree test is shown in Figure 1, with the 12 components' numbers in order of
subtraction on the x-axis and the eigenvalues on the y-axis. Greater eigenvalue factors are recovered first,
and then smaller factors. The number of factors that are stored is determined using a scree grid. Here, the
plot demonstrates that two components account for most of the total variation in the data and have
eigenvalues greater than 1. Other factors were deemed insignificant since they only contributed a small
percentage to the variance's explanation.

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Figure1: Scree Plot

Table3: Communalities of extraction

The first component, titled "Effective Time Management," is made up of seven things that aim to reduce
time, produce precise and dependable results, advance personal development, and have a good workload
impact. With an eigenvalue of 5.091, the component that effectively managed time explained 23.03% of
the overall variation. This component included seven things total, but based on its communalities, the
performance, critical thinking abilities, and burden reduction seem to be highly agreeable. Based on their
average score on the scale, the other three items—accuracy, efficiency, and time reduction—tend to move
towards agreement.

With an eigenvalue of 1.347, the second component, "Transparency," explained 22.951% of the variance.
This component included seven elements, including decision-making, ethical use, explainable, and
understandable. Large data sets tend to significantly agree with the item "decision making," but other things
with mean scale scores that are "understandable," "explainable," "decision making in complex situations,"
"clarity," and "powerful tool for acquiring" tend to agree.

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Interpretative Phenomenological Analysis (IPA)

Within the realm of education, AI tools provide edge to both learner and educator. As AI tools continues
to integrate into educational settings, understanding its impact is crucial for optimizing teaching and
learning outcomes. By examining the perspectives of various stakeholders, this research aims to uncover
the multifaceted advantages of AI tools, shedding light on their potential to enhance learning experiences,
improve pedagogical practices, and foster innovation within educational environments. Through thematic
analysis, this study seeks to identify key themes that encapsulate the diverse benefits of AI tools, paving
the way for informed decision-making and effective utilization in educational sector. Following are the
themes Identified:
Productivity
AI Tools enhances productivity due to their ability to automate tasks, optimize processes, and provide
valuable insights, allowing individuals to accomplish more with greater efficiency and effectiveness.
Dr. Jatinder, a professor is finding AI tools helpful in spreadsheet modelling and mentioned that:
AI help in spreadsheet modelling and enhances our productivity and there are many tools in AI which are
helping us in our field
Dr. Ajay Singh, a Asst.Professor is using AI enabled tools mentioned that:
We do use AI enabled tools like Co-Pilot Pro and that is something I developed a taste for other than
ChatGPT and yes they are helpful in increasing productivity.
Creativity
AI improves creativity by offering novel insights, generating diverse ideas, and facilitating exploration,
empowering individuals to experiment and create innovative solutions and push the boundaries of
traditional thinking.
Navneet, an asst. professor who is using AI tools on his mobile is finding helpful in innovation and
exploring data on the go and mentioned that:
I am using AI tools on my mobile and is helping me to innovate and explore more.
Miss Ruchi, an asst. professor had a contra view and mentioned that:
AI tools like ChatGPT are facilitating in personalized learning and enabling the exploration of new ideas
and concepts.
Content Creation
AI tools are creating a bigger space in content creation which involves generating high-quality text, images,
and multimedia content with speed and accuracy, leveraging natural language processing, image
recognition, and other advanced algorithms to meet diverse communication needs efficiently.
Dr. Ayushi who is an asst professor mentioned that:
I am using couple of things, recently I am using like Aaspaas for creating stickers, videos and all that stuff.
Language
AI helping in improving grammar, vocabulary by providing instant feedback, personalized learning
experiences, and interactive language exercises. It indicates AI tools provide potential to enhance
communication skills and written expression for learners.
Dr. Monika Kalani, an asst. professor often uses Cloud AI for text refinement and mentioned that:
If I must refine text to a greater extend, then I often use Cloud AI.
Dr. Pratap mentioned that:
I am exploring AI tools like Grammarly for improving grammar.
Dr. Pooja, an asst. professor mentioned that:
I am often using Grammarly and chatGPT in honing linguistic skills.

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Integration
AI is an advanced technology, and the integration of AI-enabled tools presents challenges in the beginning
due to the unfamiliarity with the technology, lack of training opportunities, requiring comprehensive
support systems and user-friendly interfaces to facilitate smooth adoption and proficiency development.
Jatinder, an asst professor mentioned his experience and challenges while implementing AI enabled tools
for spreadsheet modelling for smoother integration pathways.
Initial challenges were how to integrate AI in spreadsheet modelling and how to use that tools to do our
projection in a better way.
Ajay Singh, an asst. professor who have not find much challenge in integration but emphasizes on the
importance of understanding the tools to get best out of them. He mentioned that:
It’s a new trajectory so it takes time to understand, how to get the best out of these tools, we don’t know,
we must learn, it’s a long process.
Passivity and dependence
AI tools may foster passivity and dependence by providing instant solutions, reducing critical thinking, and
encouraging reliance on automated processes. Monika Kalani, Asst. Professor was concerned and showed
the need to necessitate efforts to promote active engagement, autonomy, and critical thinking skills to
mitigate these challenges and foster independent learning and problem-solving abilities. She mentioned
that:
Student performance has gone down and dependency on AI is increasing. It should be used as starting
point or for further exploring rather than for copy pasting.
Dr. Pratap raise concerns about the potential for AI-facilitated plagiarism and misuse, undermining
authentic learning and mentioned that:
AI is providing instant solutions to students which they are misusing. AI facilitating plagiarism and
cheating.
Jatinder, an asst. professor mentioned that:
AI is disrupting the natural flow of learning and critical thinking skills development and acting as a
hindrance.
Suchi, asst professor highlights concerns about stifled creativity in students by the use of AI. She mentioned
that:
AI inhibiting exploration, experimentation and killing creativity by providing instant solutions.
Monitored AI Use
Educators advocate for monitored AI use in students to ensure responsible and ethical utilization, mitigate
risks of over-reliance or misuse, and promote a balanced approach to learning that fosters critical thinking
and skill development alongside technological integration.
Anand, asst. professor mentioned that:
There is a urgent need to balance student’s autonomy and exploration with guided supervision when
utilizing AI tools, promoting a supportive environment for meaningful learning outcomes.
Kanchan mentioned that:
Monitored use of AI is crucial in cultivating digital citizenship skills among students, emphasizing
responsible online behaviour, critical evaluation of AI-generated content, and ethical decision-making in
a rapidly evolving technological landscape.
Awareness Campaigns
Neha, an asst. professor mentioned that:
Students should be encouraged to use AI for innovation, problem solving and collaborative learning in a
responsible manner.
Dr. Nitin Goyal, an asst professor mentioned that:
Definitely yes, to promote AI and run awareness campaigns. Promoting AI campaigns can help students
by raising awareness in exploring AI technologies to drive innovation and solve real-world problems.

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Discussion and conclusion

This study aims to determine the primary factors influencing the research by analyzing the survey's design.
The Kaiser-Meyer-Olkin decision score measurement and Bartlett's sphericity test were employed to
investigate the possibilities of analyzing the data set. It is possible to draw the conclusion that analysis is a
useful tool for identifying the key ideas that underpin student differences based on the findings of this
investigation. To assess the study's impact, principal components analysis and the varimax orthogonal
factor rotation approach were used to extract the two key themes of effective time management and
transparency. The consolidation of IPA results shows that it is more important for teachers for students to
be creative and imaginative. We also saw that they used intellectual skills to create content, which helped
them prepare for classes.

Limitations and Future research

Since this study's findings cannot be applied to the entire population, a larger sample can be obtained via
the sampling technique to support further investigation. However, more research is needed for detailed
analysis before strong decisions can be made to influence student knowledge to promote transparency and
control. From the teacher's perspective, this type of qualitative research has some limitations. First, this
research is exploratory in nature and aims to investigate the impact of intelligence in education. Therefore,
the findings cannot be generalized, and it is recommended to adopt quantitative or mixed methods in future
studies. Second, when participants provide new insights into the use of AI, the effectiveness of AI tools
from the learner's perspective will be an important contribution to this new research.

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