Optimising Lifelong Learning for Adults: A Chatbot-Assisted Samvada Framework for Self-Directed Learning

Samvāda, a concept from the Upanishads and traditional Indian educational practices, represents a dialogic method for exploring complex ideas. In a rapidly changing world, where learners must continually develop new skills, self-directed learning has become a vital skill. This paper reinterprets Samvāda as a tool for self-learning in the digital era through AI chatbots. To harness this potential, the study presents a chatbot-based Samvāda framework with six elements: questioning, cross- questioning, cross-referencing, summarising, validation, and self-assessment. These components aim to transform chatbot interactions from simple exchanges into structured, learner-centred dialogues. This approach also aligns with Knowles’ principles of Self-Directed Learning (SDL), where learners identify their needs, set goals, locate resources, and evaluate outcomes as active participants in their own learning journey. The study employed a pre-test–post-test control group design. This design enabled an evaluation of whether a guided conversational model could provide more meaningful learning experiences than unstructured approaches. By bridging ancient methods of inquiry with modern technologies, the study highlights the potential of Samvāda to promote the self-directed learning abilities of students in higher education.

Authors

Publication Details

Journal
International Journal of Social Science and Human Research
Published
2025-09-30
DOI
https://doi.org/10.47191/ijsshr/v8-i9-85
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimising Lifelong Learning for Adults: A Chatbot-Assisted Samvada Framework for Self-Directed Learning

Sudha Hegde, J M Chandunavar
International Journal of Social Science and Human Research
Online Learning and Analytics
article

Optimising Lifelong Learning for Adults: A Chatbot-Assisted Samvada Framework for Self-Directed Learning

Sudha Hegde, J M Chandunavar
article en

Abstract

Samvāda, a concept from the Upanishads and traditional Indian educational practices, represents a dialogic method for exploring complex ideas. In a rapidly changing world, where learners must continually develop new skills, self-directed learning has become a vital skill. This paper reinterprets Samvāda as a tool for self-learning in the digital era through AI chatbots. To harness this potential, the study presents a chatbot-based Samvāda framework with six elements: questioning, cross- questioning, cross-referencing, summarising, validation, and self-assessment. These components aim to transform chatbot interactions from simple exchanges into structured, learner-centred dialogues. This approach also aligns with Knowles’ principles of Self-Directed Learning (SDL), where learners identify their needs, set goals, locate resources, and evaluate outcomes as active participants in their own learning journey. The study employed a pre-test–post-test control group design. This design enabled an evaluation of whether a guided conversational model could provide more meaningful learning experiences than unstructured approaches. By bridging ancient methods of inquiry with modern technologies, the study highlights the potential of Samvāda to promote the self-directed learning abilities of students in higher education.

International Journal of Social Science and Human ResearchVol. 08(09)
Openalex Percentile: Top 69%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.