Article: Design and development of mobile learning UI based on situational cognition theory Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.58 pp.23 - 44 Abstract: In the context of the current inefficiency in mobile learning application interface design and the difficulty in adapting to diverse user scenarios, this study explores ways to enhance UI generation effectiveness and user experience through automation technology. By transforming the principles of environmental interaction and dynamic cognition emphasised in situational cognition theory into computable neural network components, an encoder-decoder model based on CNN and transformer is constructed. This model introduces two-dimensional spatial position encoding in the encoder to simulate users' spatial perception of interface layout, and utilises an attention mechanism in the decoder to achieve dynamic adaptation to different task scenarios. Experiments show that this method achieves a BLEU-4 score of 82.4% on the RICO dataset, with the edit distance reduced to 7.1. Furthermore, its performance degradation is minimal after adding noise and blur interference, demonstrating good robustness. In practicality evaluation, the generated interface received a comprehensive score of 4.23 from participants, especially receiving the highest recognition among the mobile learning teacher group. The method proposed in this paper effectively achieves accurate and stable generation from high-fidelity images to interface trees, providing a solution with both theoretical guidance and practical value for the automated design of mobile learning UIs. Inderscience Publishers - linking academia, business and industry through research

Title: Design and development of mobile learning UI based on situational cognition theory

Authors: Chen Liu

Addresses: College of Art, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China

Abstract: In the context of the current inefficiency in mobile learning application interface design and the difficulty in adapting to diverse user scenarios, this study explores ways to enhance UI generation effectiveness and user experience through automation technology. By transforming the principles of environmental interaction and dynamic cognition emphasised in situational cognition theory into computable neural network components, an encoder-decoder model based on CNN and transformer is constructed. This model introduces two-dimensional spatial position encoding in the encoder to simulate users' spatial perception of interface layout, and utilises an attention mechanism in the decoder to achieve dynamic adaptation to different task scenarios. Experiments show that this method achieves a BLEU-4 score of 82.4% on the RICO dataset, with the edit distance reduced to 7.1. Furthermore, its performance degradation is minimal after adding noise and blur interference, demonstrating good robustness. In practicality evaluation, the generated interface received a comprehensive score of 4.23 from participants, especially receiving the highest recognition among the mobile learning teacher group. The method proposed in this paper effectively achieves accurate and stable generation from high-fidelity images to interface trees, providing a solution with both theoretical guidance and practical value for the automated design of mobile learning UIs.

Keywords: situational cognition; mobile learning; UI design; development.

DOI: 10.1504/IJICT.2026.153808

International Journal of Information and Communication Technology, 2026 Vol.27 No.58, pp.23 - 44

Received: 19 Dec 2025
Accepted: 02 Feb 2026
Published online: 26 May 2026 *

Title: Design and development of mobile learning UI based on situational cognition theory

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