Low-code development platforms (LCDPs) are transforming business practices by shifting the focus from traditional, code-intensive approaches to business-centered modeling. These platforms enable citizen developers-non-technical employees within organizations-to build and manage applications that address specific business needs. This democratization accelerates time-to-market and encourages agile, co-participatory development. However, the rise of citizen development also introduces challenges, such as risks to quality, security, and governance, due to limited technical expertise among some users. This paper investigates ways to enhance current low-code practices by integrating AI-based support for text-to-model generation and established business frameworks, such as the Business Model Canvas (BMC). Incorporating BMC into low-code platforms reinforces their core strengths-minimizing code dependency while grounding development in business models. This integration can offer a structured pathway for citizen developers to engage in meaningful learning while ensuring their projects align with organizational objectives. This approach positions low-code not only as a productivity tool aiming faster time to market, but as platforms for continuous learning and strategic alignment with business. The proposed integrations build on a novel feedback-inclusive approach, which received the innovative feedback nomination at the University of Leuven, Belgium1, and was informed by evidence-based learning experiences at the University of Twente, Netherlands.

Abstract Student feedback analysis is time-consuming and laborious work if it is handled manually. This study explores the use of a new deep learning-based method to design a more accurate automated system for analysing students’ feedback (called DTLP: deep learning and teaching process). The DTLP employs convolutional neural networks (CNNs), bidirectional LSTM (BiLSTM), and attention mechanism. To the best of our knowledge, a deep learning-based method using a unified feature set, which is representative of word embedding, sentiment knowledge, sentiment shifter rules, linguistic and statistical knowledge, has not been thoroughly studied with regard to sentiment analysis of student feedback. Furthermore, DTLP uses multiple strategies to overcome the following drawbacks: contextual polarity; sentence types; words with similar semantic context but opposite sentiment polarity; word coverage limit of an individual lexicon; and word sense variations. To evaluate the DTLP, we conducted an experiment on a large volume of students’ feedback. The results showed (i) DTLP outperforms the existing systems in the field, (ii) DTLP that learns from this unified feature set can acquire significantly higher performance than one that learns from a feature subset, (iii) the ensemble of sentiment shifter rules, word embedding, statistical, linguistic, and sentiment knowledge allows DTLP to obtain significant performance, and (iv) an attention mechanism into CNN-BiLSTM improves the performance of DTLP. In addition, the deployed method looks for potential causes behind student feedback.

Requirements analysis and modeling is a challenging task involving complex knowledge of the domain to be engineered, modeling notation, modelling knowledge, etc. When constructing architectural artefacts experts rely largely on the tacit knowledge that they have built based on previous experiences. Such implicit knowledge is difficult to teach to novices, and the cost of the gap between classroom knowledge and real business situations is thus reflected in further needs for post-graduate extensive trainings for novice and junior analysts. This research aims to explore the state-of-the art natural language processing techniques that can be adopted in the domain of requirements engineering to assist novices in their task of knowledge construction when learning requirements analysis and modeling. The outcome includes a method called Text-To-Model (TeToMo) that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture elemen t candidates out of textual descriptions (business requirements). A subsequent prototype is implemented that can assist a knowledge construction process through (semi-) automatic generation and validation of Unified Modeling Lnaguage (UML) models. In addition, to the best of our knowledge, a method that integrates machine learning based method has not been thoroughly studied for solving requirements analysis and modeling problem. The results of this study suggest that integrating machine learning methods, word embedding, heuristic rules, statistical and linguistic knowledge can result in increased number of automated detection of model constructs and thus also better semantic quality of outcome models.

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Providing individual and immediate feedback to students is a critical factor for improving knowledge and skills acquisition in higher education. However, a growing number of students with very different heterogeneous profiles and unequal problem solving skills, as well as the lack of teaching resources, make it very challenging and sometimes even impossible to give immediate feedback at individual level. This paper presents a synthesis and progress of a long-term project that addresses this challenge in the context of conceptual modelling by developing SAiLE@CoMo, a smart and adaptive learning environment. By crafting innovative process analytics techniques and expert knowledge on feedback automation, SAiLE@CoMo can automatically provide personalized and immediate feedback to leaners.

The complexity of model-driven engineering leads to a limited adoption of MDE in practice. In this paper we argue that MDE offers "low hanging fruit" if creating executable UML models is targeted rather than developing full-fledged information systems. This paper describes an environment for designing and validating conceptual business models using the model-driven architecture (MDA). The deliverable of the proposed modelling environment is an executable platform independent model (EPIM) that is further tested and validated through an MDA-based simulation feature. The proposed environment addresses a set of challenges associated with 1. shortcomings of the UML for being technically too complex for conceptual modelling goals as well as for being not precise enough for rapid prototyping; 2. difficulties of MDE adoption due to the large set of required skills to adopt the key MDA standards such as the UML and XMI for developing and maintaining a requirements prototyping tools. The paper aims to introduce the current work and identify the needs for future research.