Pre-Service Indonesian Language Teachers’ Needs and Readiness for a Local Wisdom-Based Pedagogical Chatbot

Zahra Alwi; Khalidatun Nuzula; Hani Atus Solihah; Astrid Yulinda Putri; Chrisnatama Tangguh Prasetyo

Authors

Zahra Alwi Program Studi Pendidikan Bahasa dan Sastra Indonesia, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sriwijaya, Indonesia
Khalidatun Nuzula Program Studi Pendidikan Bahasa dan Sastra Indonesia, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sriwijaya, Indonesia
Hani Atus Solihah Program Studi Pendidikan Bahasa dan Sastra Indonesia, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sriwijaya, Indonesia
Astrid Yulinda Putri Program Studi Pendidikan Bahasa dan Sastra Indonesia, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sriwijaya, Indonesia
Chrisnatama Tangguh Prasetyo Program Studi Pendidikan Bahasa dan Sastra Indonesia, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sriwijaya, Indonesia

Keywords:

Pedagogical Chatbot; Local Wisdom; Pre-Service Teachers; Technology Acceptance; Needs Assessment

Abstract

Pre-service Indonesian language teachers frequently struggle to translate Learning Outcomes (Capaian Pembelajaran) into concrete Learning Objectives and Trajectories (Tujuan and Alur Tujuan Pembelajaran), while South Sumatran local wisdom remains largely absent from their lesson designs and unsupervised generative-AI use further erodes critical digital literacy. This study reports a preliminary needs assessment and formative pilot evaluation of PAMONG-AI, a Socratic, local-wisdom-based pedagogical chatbot developed to accompany the Lesson Planning for Indonesian Language and Literature course at Universitas Sriwijaya. A sequential mixed-methods design combined a 20-item baseline competency test (N = 73) with a 51-item post-trial questionnaire (N = 80) covering technology acceptance, Socratic-interaction quality, local-wisdom integration, curriculum self-efficacy, usability, and dissemination readiness, supplemented by open-ended responses analyzed thematically. Results showed low baseline mastery (M = 33.8%, SD = 10.98), with the weakest areas being Deep Learning pedagogical principles (5%), learning-model syntax (9%), Modul Ajar structure (14%), and Kurikulum Merdeka/Kurikulum Berbasis Cinta distinctions (18%), precisely the gaps the chatbot was designed to close. After piloting the prototype, students reported favorable technology acceptance (M = 3.62/5), valued its Socratic questioning (M = 3.54/4) and local-wisdom integration (M = 3.54/4), and indicated improved curriculum self-efficacy (M = 3.53/4), while usability scored moderately (adapted SUS = 56.8/100), signalling concrete areas for refinement before expert validation. These findings validate the chatbot’s design rationale and provide an empirical foundation for its next development iteration.

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