=Paper=
{{Paper
|id=Vol-3783/paper_326
|storemode=property
|title=EduClare – An Intelligent Tutoring Chatbot for Teaching Declarative Process Modeling
|pdfUrl=https://ceur-ws.org/Vol-3783/paper_326.pdf
|volume=Vol-3783
|authors=Sabine Nagel,Anna Wolters,Dennis M. Riehle,Patrick Delfmann
|dblpUrl=https://dblp.org/rec/conf/icpm/NagelWRD24
}}
==EduClare – An Intelligent Tutoring Chatbot for Teaching Declarative Process Modeling==
https://ceur-ws.org/Vol-3783/paper_326.pdf
EduClare – An Intelligent Tutoring Chatbot for
Teaching Declarative Process Modeling
Sabine Nagel1,* , Anna Wolters1 , Dennis M. Riehle1 and Patrick Delfmann1
1
University of Koblenz, Universitätsstr. 1, 56070 Koblenz, Germany
Abstract
In this work, we introduce EduClare, a web-based intelligent tutoring chatbot to support Declarative
Process Model (DPM) education in the modeling language Declare. This innovative hybrid chatbot
combines principles from intelligent tutoring systems and large language models to create an interactive
and user-friendly educational tool. In addition to explaining fundamental concepts of Declare, the
chatbot generates an arbitrary number of tasks with increasing difficulty levels, covering basic reasoning,
model execution, and active modeling of declarative specifications, with a focus on constraint interplay
and inconsistency. Users benefit from instant answer validation, feedback, and the ability to ask for
clarification, receiving customized guidance throughout their entire learning process.
Keywords
Declare, Declarative Process Models, Education, Intelligent Tutoring Systems, Chatbot
Metadata description Value
Tool name EduClare
Current version 1.0
Legal code license CC 4.0 BY-NC-ND
Languages, tools and services used Java, JavaScript, LangChain4j, gpt-4o
Supported operating environment N/A
Download/Demo URL https://educlare.de/
Documentation URL https://uni-ko.de/educlare-git
Source code repository https://uni-ko.de/educlare-git
Screencast video https://uni-ko.de/educlare-screencast
1. Introduction and Related Work
Declarative Process Models (DPMs) offer a flexible approach to process modeling by specifying a
set of constraints that implicitly define process behavior rather than explicitly modeling a fixed
sequence of activities [1]. However, the implicit nature of DPMs introduces significant challenges
in comprehension and application [2], which stresses the need for effective educational tools.
Existing approaches, such as the one developed by De Smedt et al. [3] primarily focus on
ICPM 2024 Tool Demonstration Track, October 14-18, 2024, Kongens Lyngby, Denmark
*
Corresponding author.
$ snagel@uni-koblenz.de (S. Nagel); awolters@uni-koblenz.de (A. Wolters); riehle@uni-koblenz.de (D. M. Riehle);
delfmann@uni-koblenz.de (P. Delfmann)
0000-0003-4838-8246 (S. Nagel); 0000-0002-4075-5737 (A. Wolters); 0000-0002-5071-2589 (D. M. Riehle);
0000-0003-4441-0311 (P. Delfmann)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
exploring hidden dependencies within DPMs. More recently, Nagel and Delfmann [4] proposed
an e-learning framework designed to support learners in gradually becoming familiar with
Declare. This framework addresses the common challenges identified in understanding Declare
by offering a structured set of tasks that cover various aspects of the modeling language.
However, there remains a significant gap in the availability of interactive, technology-enhanced
educational tools for teaching Declare.
Within the field of technology-enhanced education, Pedagogical Conversational Agents
(PCAs) [5] are widely used. PCAs, which are conversational agents (CAs) applied in educational
contexts, offer an interactive learning experience that aligns with the ICAP framework, em-
phasizing the importance of active engagement in learning [6]. Recent advancements in CAs,
particularly those using Large Language Models (LLMs), have opened new possibilities for in-
creasing the interactivity and effectiveness of educational tools. While CAs have been explored
in the context of business process modeling, their application to declarative process modeling
has been relatively limited. Notable exceptions include Declo, a chatbot introduced by Alman
et al. [7] for defining DPMs using natural language, and the CA developed by Fontenla-Seco
et al. [8] for supporting declarative process mining. Still, the development of tools for DPM
education remains unexplored.
To address this gap, we present EduClare, an intelligent tutoring chatbot designed to teach
the declarative modeling language Declare. Our solution applies the latest advances in CA
research and provides a structured, task-based learning experience that adapts to the learner’s
progress. The remainder of this paper is structured as follows. In Section 2 we describe the
application, its functionality, and architecture in more detail. In Section 3 we explain usage and
report on the results of a first usability study to discuss the tool’s maturity. We conclude and
discuss directions for future work in Section 4.
2. Tool Description
2.1. Functionality
EduClare is a comprehensive educational tool designed to support learning Declare through an
interactive and personalized experience. The tutoring chatbot guides users through three main
phases: (1) introducing the tutoring and explaining relevant Declare concepts, (2) providing
tasks of different types and with increasing difficulty as part of the task-based learning and (3)
interactively answering questions to provide continuous support. The task-based learning phase
builds on previous work [4], where we developed the foundation for an e-learning concept for
teaching Declare and provided a comprehensive collection of tasks covering concepts such as
constraint definitions, model behavior, and inconsistency within DPMs. In the following, we
will describe the tool’s functionality in more detail.
Introduction In this phase, the chatbot assesses the user’s prior experience with Declare. New
users are provided with a detailed introduction, while more experienced users can choose
to skip this section or only refresh their prior knowledge. This phase is interactive, with
the chatbot regularly prompting users to ask questions to ensure that uncertainties are
immediately addressed.
Figure 1: Exemplary Chatbot Conversations
Task Generation The chatbot can dynamically generate an arbitrary number of tasks with
varying difficulty levels to help users improve and test their understanding of Declare.
This includes (1) inference tasks where users must infer an activity based on a provided
model and corresponding statement, (2) execution tasks, where users are required to
provide valid/invalid traces for a model and vice versa, (3) inconsistency tasks where users
are provided with a consistent model and either have to maintain consistency or cause
inconsistency by adding a constraint and (4) redundancy tasks where users are asked to
add or remove constraints to/from a model without changing its behavior. Furthermore,
EduClare currently supports three task types (true/false, choice and input) to reinforce
comprehension through both recognition and recall.
Instant Answer Validation and Feedback Any answers given by the user are evaluated on
the fly and the chatbot provides instant feedback on each task, indicating whether the
answer was correct and providing explanations when mistakes were made (cf. Figure 1).
Customized Guidance and Continuous Support Users can interrupt the task flow at any
time to interact with the chatbot and receive customized guidance, as illustrated in Figure
1. Here, possibilities include asking for a new task, an exemplary answer or hint, and
asking any other task-related questions, such as the definition of Declare templates used
in a task. The chatbot’s ability to maintain conversation history allows it to provide
contextually relevant answers and continue discussions seamlessly.
Learning Progression The chatbot supports learning progression as the tutoring process
is designed based on levels, allowing users to gradually build their skills. Each level is
designed to incrementally increase in difficulty (beginner, intermediate, and expert) and
interaction complexity (true/false, choice, input). Eligibility to advance to a new level is
based on a pre-defined number of consecutive correct answers, but users can decide to
keep practicing at any point, which enables continuous learning.
2.2. Architecture
EduClare is implemented as a Java SpringBoot web application. To integrate LLMs, we used the
LangChain4j1 framework and selected OpenAI’s GPT-4o model as the underlying LLM. We will
now describe the architecture and design of our application (cf. Figure 2) in more detail.
Intent ASSISTANTS TOOLS ALGORITHMS
Recognition
Output Task Generator
Parsing Task Generation
Generate Task Task
Assistant
Generation
Trace Validation
Evaluation Assistant Task Evaluator
Process Solution
Service Selector Answer Language
Attempt Evaluation
Identification Comparison
Check for Empty
General Assistant Language
Answer Task-Related Tools
Generation
Figure 2: Overview of Chatbot Architecture
Frontend The frontend was developed using JavaScript, HTML and CSS. It allows the user to
interact with the tutoring chatbot, either in textual form via the input field or by selecting
one of the provided options. The chatbot additionally allows customized interactions,
such as allowing users to input shorthand commands or full sentences, which are then
correctly interpreted by the system. This allows for more flexibility and accessibility.
Assistants Assistants manage the interaction between users and the system’s underlying tools
and algorithms. The Service Selector is the first to process user messages by determining
their intent, i.e., whether it is requesting a new task, responding to a task, or asking
a general question. Based on this intent, the Service Selector directs the request to the
appropriate assistant. The Task Generation Assistant handles requests for new tasks
by invoking the Task Generator Tool, formatting the output for user display. The Task
Evaluation Assistant manages task responses by identifying and evaluating user-provided
artifacts using specific algorithms. The General Assistant addresses all other queries, such
as answering general questions or providing exemplary answers, relying on predefined
tools or the language model for more complex responses. To increase the validity of the
answer to any general question, a system prompt was designed that provides the LLM with
general information on Declare as well as further instructions on how to communicate
with the users. Therefore, any general question that is not covered by a tool is directly
1
https://docs.langchain4j.dev
processed by the prompted LLM. Each assistant ensures user interactions are efficiently
processed and appropriately handled.
Tools The tools within our tutoring chatbot architecture perform specific tasks as directed by
the assistants. They are defined by natural language descriptions that enable assistants to
select and invoke the correct function for user requests. For example, the Task Generator
Tool creates new tasks based on user context, while the Task Evaluation Tool validates
user responses by comparing them with expected outcomes or calling the corresponding
evaluation algorithm. This ensures efficient system performance by reducing reliance on
the LLM for straightforward tasks. Lastly, tools accessed by the General Assistant handle
routine requests, like providing exemplary answers or repeating tasks.
Algorithms We developed various Java algorithms to generate and evaluate tasks. For task
generation, we created specific algorithms tailored to each task category, which enables
the chatbot to create arbitrary amounts of tasks covering a variety of Declare-related
concepts with progressively increased difficulty. To generate and assess both correct and
incorrect answers, we employed additional algorithms using deterministic finite automata
(DFA) via dk-brics-automaton2 . By transforming each Declare model into a corresponding
DFA, we validated traces against models, compared languages of different models (e.g., to
check for redundant constraints), and ensured model consistency (as inconsistent models
have an empty language).
3. Usage and Maturity
A demo version of EduClare is available here3 , a local version can be obtained from our Git
repository and requires an own OpenAI API key.
To gain first insights into the usability of our initial concept, we conducted a small study with
four PhD students that had varying levels of prior experience with Declare. It comprised of an
exploration phase, where the participants were instructed to try out the chatbot on their own
and explore its functionality, usability and intuitiveness, while voicing their thoughts . To gain
additional insights, we then asked some follow-up questions, focusing on encountered difficulties,
suggestions for improvement or extension and general feedback. Overall, the tutoring chatbot,
its innovative approach and overall usability was perceived positively. Users appreciated the
chatbot’s ability to make Declare more accessible, with two participants mentioning that they
found the interaction with the chatbot more engaging and enjoyable compared to conventional
methods, such as textbooks or literature. The chatbot’s guidance throughout the learning
process was also well-received, with one participant mentioning an increased curiosity to
explore beyond the necessary tasks, indicating a high level of user engagement. Additionally,
some feedback suggested areas for improvement, such as shortening introductory messages and
incorporating visual aids, as well as further customization based on individual learning styles.
Furthermore, suggestions were made for introducing gamification elements and adjusting task
difficulty based on user experience. The feedback also indicated interest in including visual
representations of Declare, which could be a direction for future development.
2
https://www.brics.dk/automaton/
3
https://educlare.de/
4. Conclusion
In this work, we developed an intelligent tutoring chatbot with a hybrid architecture to support
Declare education. Our approach integrates a structured learning path with task-based learning
and continuous interactive support, tailored to different experience levels. The LLM-based
chatbot effectively generates and evaluates tasks, while assisting users in learning Declare.
Future work will address current limitations, such as a risk of producing incorrect outputs,
by iteratively improving and expanding the chatbot’s functionality. This includes visual DPM
representations, multilingual support, and personalized learning experiences. That way, we aim
to not only enhance Business Process Management (BPM) education but also provide a valuable
tool for process mining researchers.
Acknowledgments
This paper was funded by the Deutsche Forschungsgemeinschaft (grant number DE 1983/9-3)
and the Federal Ministry of Education and Research (BMBF) under grant number 16DHBKI039.
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