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Title: Balancing Artificial Intelligence and Human Insight in Early Childhood Education: Implications for Child Development
Authors: Abdul Qayyum, Maryam Bukahri, Pakiza Zulfiqar, Maryam Ramzan
Journal: Social Science Review Archives
| Category | From | To |
|---|---|---|
| Y | 2024-10-01 | 2025-12-31 |
Publisher: Divine Knowledge Institute
Country: Pakistan
Year: 2024
Volume: 2
Issue: 2
Language: English
Keywords: AI in EducationEarly Childhood EducationAI-Driven AssessmentTeacher PerceptionCognitive Development
Artificial intelligence technology is increasingly integrated into education, offering potential benefits for personalized feedback and data-driven insights. However, its effectiveness in early childhood education, particularly in terms of teachers’ perceptions and experiences, remains underexplored. This study aimed to evaluate the effectiveness of AI-driven tools in early childhood education, focusing on learning outcomes, usability, feedback quality, and teacher workload in preschool and kindergarten settings. A mixed-methods design was used, comprising a survey of 40 teachers and semi-structured interviews with 10 participants. Quantitative data were analyzed through descriptive statistics and ANOVA, while qualitative data were analyzed using thematic analysis. 80% of teachers believed AI tools enhance learning outcomes, with experienced teachers more favorable toward AI feedback. Challenges included AI's inability to interpret social cues, emphasizing the need for human interaction in early learning. AI tools should complement human teaching, with a focus on teacher training and balancing AI with human interaction in early education.
To evaluate the effectiveness of AI-driven tools in early childhood education, focusing on learning outcomes, usability, feedback quality, and teacher workload, and to propose a hybrid model integrating AI tools with human expertise.
A mixed-methods design was employed, including a survey of 40 teachers and semi-structured interviews with 10 teachers. Quantitative data were analyzed using descriptive statistics and ANOVA, while qualitative data underwent thematic analysis.
graph TD; A["Survey Data Collection"] --> B["Descriptive Statistics & ANOVA"]; C["Interview Data Collection"] --> D["Thematic Analysis"]; B --> E["Synthesize Quantitative & Qualitative Findings"]; D --> E; E --> F["Formulate Conclusions & Recommendations"];
AI tools are perceived positively for improving learning outcomes and providing useful feedback, particularly by experienced teachers. However, usability can be a challenge for less experienced educators. The critical role of human interpretation, emotional support, and understanding social dynamics in early childhood education was emphasized, suggesting AI should augment, not replace, human educators. Satisfaction with AI feedback varied across schools, indicating the influence of contextual factors.
80% of teachers believed AI tools enhance learning outcomes. Experienced teachers were more favorable toward AI feedback. Challenges included AI's inability to interpret social cues, highlighting the need for human interaction. AI tools should complement human teaching, with a focus on teacher training and balancing AI with human interaction.
AI-driven tools offer potential benefits in early childhood education, but their successful implementation hinges on addressing teacher experience, usability, and the fundamental need for human interpretation and teacher-student relationships. Ongoing teacher training, resource allocation, and thoughtful integration are crucial.
- 80% of teachers agreed that AI tools enhance educational performance.
- Experienced teachers rated AI tools as more effective for improving learning outcomes (M = 4.4) compared to less experienced teachers (M = 4.0).
- The study involved a survey of 40 teachers and interviews with 10 teachers.
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