Revolutionizing education: Artificial intelligence empowered learning in higher education

Figures & data

Table 1. Summary of previous studies on AI in higher education

Objectives	Methods	Research Objects	Key Findings	References
Application of AI and Learning Environment	Surveys, Interviews	Faculty members	Teachers’ motivation to apply AI depends upon the problem presented by AI. Application is AI in HE depends upon its opportunities.	(Bucea-Manea-Țoniş et al., 2022; Chan, 2023)
Impact of AI on curriculum design and future direction	Experimental Designs	Higher Education Institutions	Identified AI mode of teaching methods to influence students’ learning through learning Management systems and curriculum design.	(Kuleto et al., 2023)
Reforming education in an era of AI	Comparative Analysis	Higher Ed Institutions	Explored how AI adoption resulted in institutional transformations, emphasizing improved accessibility, and learning outcomes.	(Qu et al., 2022; Xue et al., 2022; Zang et al., 2022)
Challenges in the Implementation of AI in Higher Education	Survey and Case Studies	Universities and Colleges	Identified challenges, ethical concerns, and barriers hindering effective AI integration in higher education settings.	(Chatterjee & Bhattacharjee, 2020b; Michel-Villarreal et al., 2023; Slimi & Carballido, 2023)
Faculty Role Transition	Qualitative Analysis	Teaching Professionals	Explored the evolving role of faculty in AI-driven learning environments, emphasizing a shift toward facilitation.	(Hwang et al., 2020; Naylor & Nyanjom, 2021)

Source: Literature Review; Scopus and Science Direct Database.

Table 2. Narration and description of variables

Variables	Narration/Description
FC -Facilitating conditions	Facilitating conditions -refers to the extent to which an individual perceives that technical and related infrastructure are readily accessible and capable of supporting the utilization of AI in the system
AW-Awareness	Awareness about AI-based academic tools for routine academic activities.
PR – Perceived Risk	Perceived Risk refers to the belief that a user might experience an unfavorable outcome or loss while pursuing a particular outcome especially associated with a web-based technology like AI. It is closely linked with behavioral insecurities related to volatility associated with internet connections and functions.
PE- Performance Expectancy	Performance Expectancy refers to the relative advantage or usefulness of a new system like AI in the education arena.
EE – Effort Expectancy	Effort Expectancy refers to the simplicity or ease of use of a new system and is a significant predictor of Attitude.
A- Adoption	The adoption of AI for the improvement of society and making education cost-effective through AI-enabled learning is checked here.
AT- Attitude	To perform a target behavior positive or negative, feelings are exhibited by individuals. In this context, it refers to the attitude towards the use of new AI-based systems in the field of education.
BI - Behavioural	BI - Behavioural Intention is associated with a sense of assessing the strength of intention of an individual to perform a specific behavior.
WE- Work Engagement	Work engagement here refers to the way the classroom engagement of students is enhanced through AI-enabled tools. The faculty’s perception of improvement in the participation and performance of students through AI-facilitated pedagogy is investigated.
AI-HE	AI HE- Artificial intelligence in Higher Education – this variable refers to the way the teachers are using AI to create content for the classroom delivery, courseware, check plagiarism, and enhance the teaching experience using AI-enabled tools

Figure 1. Conceptual framework.

Source: Authors conceptual development.FC-facilitating condition; AW-awareness; PR-Perceived Risk; PE-Performance Expectancy; EE-Effort expectancy; AT-Attitude; BH-Behavior; WE-Work engagement; AAIH-Application of AI in higher Education

Figure 2. Research process.

Source: Developed by authors

Figure 3. Distribution of sampling collection.

Source: Developed by authors.

Table 3. Constructs, measures, and reliability

Constructs	No. of items	Reliability (α)	Sources
Facilitating Condition	5	0.849	(Venkatesh et al., 2003)
Awareness	5	0.799	(Davis et al., 2023)
Perceived risk	4	0.711	(Davis et al., 2023)
Performance Expectancy	5	0.835	(Venkatesh et al., 2003)
Effort Expectancy	5	0.746	(Sohn & Kwon, 2020a)
Adoption of AI for Society	4	0.845	(Chatterjee & Bhattacharjee, 2020a)
Attitude	5	0.865	(Braganza et al., 2021)
Behavior	5	0.848	(Sohn & Kwon, 2020a)
Work engagement	4	0.799	(Braganza et al., 2021)
AI in higher education	5	0.845	(Chatterjee & Bhattacharjee, 2020a)

Source: Data Analysis

Figure 4. Sampling technique.

Source: Developed by authors

Table 4. Correlation and validity

	AD	AIHE	AT	AW	BH	EE	FC	PE	PR	WE	VIF	CR	AVE
AD	0.829										3.8	0.897	0.687
AIHE	0.716	0.784									3.7	0.887	0.614
AT	0.761	0.710	0.809								3.9	0.904	0.655
AW	0.439	0.420	0.558	0.744							3.4	0.861	0.553
BH	0.758	0.731	0.792	0.510	0.790						3.9	0.892	0.624
EE	0.542	0.611	0.628	0.561	0.533	0.859					4.0	0.849	0.738
FC	0.318	0.328	0.464	0.322	0.292	0.270	0.785				4.1	0.889	0.616
PE	0.672	0.641	0.808	0.545	0.703	0.609	0.369	0.818			3.8	0.889	0.668
PR	0.165	0.101	0.189	0.008	0.252	0.062	0.115	0.207	0.586		3.3	0.636	0.543
WE	0.800	0.690	0.739	0.432	0.661	0.575	0.310	0.698	0.115	0.790	3.9	0.867	0.624

Source: Data Analysis; Note: CR: Composite reliability; AVE: Average variance extracted.

Table 5. Model fit

Mode	Structured Model	Estimated Model	Criteria
Chi-Square (X^2)	743.33	765.23	>0.01
SRMR	0.061	0.023	<0.08
NFI	0.935	0.912	>0.90

Source: Data Analysis

Figure 5. Structural model and path weight.

Source: Data Analysis

Table 6. Testing of hypothesis

Path	Hypothesis	β-value	Significance	Remarks
FC -> AT	H1	0.83	0.001	Supported
AW -> AT	H2	0.75	0.000	Supported
PR -> AT	H3	0.48	0.001	Supported
PE -> AT	H4	0.42	0.000	Supported
EE -> AT	H5	0.057	0.385	Not-Supported
AD -> AT	H6	0.480	0.000	Supported
AT-> WE	H7	0.704	0.000	Supported
WE->AIHE	H8	0.427	0.000	Supported
BH-> AIHE	H9	0.427	0.000	Supported

Source: Data Analysis

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