CD Skripsi

investigating the contribution of self-regulated learning, learning motivation and ai use to academic procrastination among english master program students of fkip universitas riau

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Academic procrastination has become a pervasive challenge in higher
education, particularly among postgraduate students facing cognitively demanding
tasks like thesis writing. This study was initiated based on preliminary findings
that many English Master’s students at FKIP Universitas Riau struggle with
delaying academic tasks, often due to internal and external pressures. Students
cited difficulties balancing academic responsibilities with work, low motivation,
and a lack of effective self-regulation. Notably, students reported increasing use of
Artificial Intelligence (AI) tools such as ChatGPT and Grammarly to manage their
academic workload. While these tools may support academic performance, their
role in either mitigating or enabling procrastination remains unclear, particularly
in the context of English as a Foreign Language (EFL) students.
The objective of this study was to determine the level and causes of
academic procrastination and to analyze the extent to which self-regulated
learning (SRL), learning motivation, and AI use individually and collectively
contribute to academic procrastination. Specifically, it aimed to identify which of
these variables significantly predict procrastinatory behavior among EFL
postgraduate students and whether AI acts as a support mechanism or a
distraction. The study also intended to fill the gap in empirical research exploring
how psychological and technological factors interact in influencing student
procrastination in Indonesian EFL academic contexts.
To achieve these objectives, the research employed a descriptivecorrelational
quantitative approach. The entire population of 48 active students in
the English Master’s Program was selected using total population sampling. Data
were collected using three validated instruments: the Procrastination Assessment
Scale for Students (PASS), selected subscales from the Motivated Strategies for
Learning Questionnaire (MSLQ), and a modified Technology Acceptance Model
(TAM) questionnaire. The PASS assessed students’ academic procrastination
behaviors and reasons, while the MSLQ captured their SRL strategies and
learning motivation. TAM was used to measure students’ perceptions of AI in
academic settings through three dimensions: perceived usefulness, perceived ease
of use, and behavioral intention. Data were analyzed using descriptive statistics
and multiple regression tests via SPSS.
The findings revealed that the level of academic procrastination among
students was moderate overall. The most frequently delayed activities were
writing term papers and completing reading assignments. Regarding reasons,
students cited time management problems, aversiveness to certain tasks, low
confidence in their ability, and lack of personal initiative. Self-regulated learning
was found to have a significant negative effect on procrastination; students who
employed better time management, effort regulation, and metacognitive strategies
were less likely to procrastinate. Learning motivation, especially intrinsic
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motivation and task value, also contributed negatively and significantly, showing
that more motivated students were better able to complete their tasks on time.
Interestingly, the use of AI showed a positive and significant contribution to
procrastination. Students who perceived AI tools as easy and useful were more
prone to delay tasks, potentially because these tools offered a sense of security or
a shortcut that reduced urgency. This suggests that AI may serve both as a support
system and as a tool for avoidance depending on how it is used.
The multiple regression analysis demonstrated that all three variables (selfregulated
learning, learning motivation, and AI use) contributed significantly to
academic procrastination when analyzed together. Of the three, SRL had the
strongest influence, followed by learning motivation. AI use, while significant,
had the smallest effect and was positively correlated, indicating its possible
double-edged role. The model highlights the importance of self-regulatory and
motivational skills in academic success, as well as the need for critical
engagement with technology.
In conclusion, academic procrastination among English Master’s students at
FKIP Universitas Riau is a multidimensional issue influenced by psychological
and technological factors. The study confirms that SRL and motivation are key
protective factors against procrastination. Meanwhile, although AI has potential
benefits, its uncritical use may reinforce avoidance behaviors. This research
contributes to the broader understanding of academic behavior by integrating
psychological constructs with technological variables, offering a practical
framework for interventions. Educators and institutions are encouraged to enhance
students’ self-regulatory and motivational capacities, while also guiding
responsible AI use in academic contexts to minimize procrastination and improve
academic performance.
Keywords: Academic Procrastiantion, AI Use, Learning Motivation, Self-
Regulated Learning

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2110246958

Penerbit

Pekanbaru : UNRI – PASCA – Magister Bahasa Inggris., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2110246958

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

MAGISTER BAHASA INGGRIS

Info Detail Spesifik

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Pernyataan Tanggungjawab

Rahmat

Versi lain/terkait

Lampiran Berkas

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