Learning to Live with Gen-AI

Topic Information

Dear Colleagues,

In 2023, in the wake of the launch of ChatGPT and based on GPT-3, we invited contributions from a wide range of MDPI journals on the Topic titled “AI chatbots: threat or opportunity?” Two years on, and the impact of Generative AI—Gen-AI—is becoming ever more apparent. The application of Gen-AI is causing consternation and uncertainty across wide swathes of society, including education, skill development, the creative arts, and employment.

In the commercial world, Gen-AI is being harnessed for applications such as customer relationship management, recruitment and career development, and strategic modelling. Governments are looking at the ways in which these technologies might produce cost-cutting and wider efficiencies in processing welfare applications, allocating resources, and managing healthcare.  

In terms of Gartner’s Hype Cycle, Gen-AI has moved beyond the initial Technology Trigger phase, where it was first seen as a breakthrough technology, generating significant media interest. The question is the extent to which this early potential and promise may or may not lead to a Peak of Inflated Expectations, where the initial spike in attention is gradually seen as overstated and hyped. It remains to be seen whether or not the subsequent phases of the Hype Cycle—Trough of Disillusionment, Slope of Enlightenment, Plateau of Productivity—will apply to Gen-AI technologies.

On the other hand, the emergence of these technologies has elicited both positive and negative responses, with many of the Gen-AI vendors proclaiming the benefits and virtues of their products and Gen-AI overall, while others painting a far more pessimistic view of the future, including many of those who were previously involved in the development of these technologies themselves.

The issues raised by Gen-AI impact a range of practices and disciplines, as well as numerous facets of our everyday lives and interactions. Hence, this invitation to submit work comes from editors associated with a wide variety of MDPI journals, encompassing a range of inter-related perspectives on the topic. We are keen to receive submissions relating to these technologies, also with regard to the wider implications of their use in social, technical, and educational contexts.

We are open to all manner of submissions, but to give some indication of the aspects of key interest, we list the following questions and issues:

• The development of Gen-AI has been claimed to herald a new era, offering significant advances in the incorporation of technology into people’s lives and interactions. Is this likely to be the case, and if so, where are these impacts going to be the most pervasive and effective?
• Is it possible to strike a balance regarding the impact of these technologies so that any potential harms are minimized, while potential benefits are maximized and shared?
• How should educators respond to the challenge of Gen-AI? Should they welcome this technology and re-orient teaching and learning strategies around it, or seek to safeguard traditional practices from what is seen as a major threat?
• There is a growing body of evidence that the design and implementation of many AI applications, i.e., algorithms, incorporate bias and prejudice. How can this be countered and corrected?
• How can the academic world and the wider public be protected against the creation of "alternative facts" by AI? Should researchers be required to submit their data with manuscripts to show that the data are authentic? What is the role of ethics committees in protecting the integrity of research?
• Can the technology underlying AI chatbots be enhanced to guard against misuse and vulnerabilities?
• Novel models and algorithms for using Gen-AI in various personalized contexts, e.g., counselling and healthcare.
• Techniques for controlling, correcting, and supervising Gen-AI.
• Evaluation methods for assessing the performance of Gen-AI.
• Case studies and experiences in developing and deploying Gen-AI in real-world scenarios.
• Social and ethical issues related to Gen-AI.

The potential impact of these technologies on the topics covered by the MDPI journals involved is twofold: on the one hand, there is a need for research on the technological bases underlying Gen-AI; on the other hand, there are many aspects relating to the support and assistance that Gen-AI can provide to designers, developers, and others operating in the many fields and practices encompassed by this collection of journals that could be explored.

Prof. Dr. Antony Bryant, Editor-in-Chief of Informatics
Prof. Dr. Paolo Bellavista, Editor-in-Chief of Computers, Section Editor-in-Chief of Future Internet
Prof. Dr. Kenji Suzuki, Editor-in-Chief of AI
Prof. Dr. Horacio Saggion, Editorial Board Member of Information
Prof. Dr. Roberto Montemanni, Section Editor-in-Chief of Algorithms
Prof. Dr. Andreas Holzinger, Editor-in-Chief of MAKE
Prof. Dr. Min Chen, Editor-in-Chief of BDCC
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Algorithms algorithms	2.6	5.4	2008	17.6 Days	CHF 1800	Submit
Big Data and Cognitive Computing BDCC	5.3	11.4	2017	23.3 Days	CHF 1800	Submit
Computers computers	5.2	9.1	2012	15.4 Days	CHF 1800	Submit
Data data	2.4	5.4	2016	19.2 Days	CHF 1600	Submit
Future Internet futureinternet	4.6	10.0	2009	15 Days	CHF 1800	Submit
Informatics informatics	5.1	9.1	2014	32.7 Days	CHF 1800	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	8.4	12.7	2019	18.7 Days	CHF 1800	Submit
Publications publications	3.4	5.7	2013	26.5 Days	CHF 1600	Submit
Smart Cities smartcities	6.6	13.0	2018	25.1 Days	CHF 2000	Submit

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Published Papers (4 papers)

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All AI Algorithms BDCC Computers Data Future Internet Informatics Information MAKE Publications Smart Cities

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29 pages, 1144 KB  

Open AccessPerspective

Cognitive Entanglement: Toward a Developmental Framework of the Human-AI Coevolutionary Leap

by Xiao-Kun Wu, Min Chen and Giancarlo Fortino

Big Data Cogn. Comput. 2026, 10(8), 261; https://doi.org/10.3390/bdcc10080261 - 5 Aug 2026

Viewed by 589

Abstract

Large language models have become routine participants in everyday cognition. Their role has widened from retrieval and text generation to helping users define problems, organize arguments, make judgments, and interpret themselves. Yet their cognitive consequences are strikingly divergent. For some users, generative AI [...] Read more.

Large language models have become routine participants in everyday cognition. Their role has widened from retrieval and text generation to helping users define problems, organize arguments, make judgments, and interpret themselves. Yet their cognitive consequences are strikingly divergent. For some users, generative AI appears to reduce critical engagement, independent judgment, and tolerance for difficulty. For others, the same class of systems becomes a medium for conceptual expansion, reflective questioning, and higher-order learning. This divergence cannot be explained by model capability alone. Mental effort is often treated as a cost to be reduced. Yet repeated delegation may also reduce opportunities to practice the processes required for independent judgment. The key issue is developmental: how sustained AI use changes users’ cognitive capacities over time. This perspective proposes cognitive entanglement as a framework for understanding the developmental consequences of sustained human-AI coupling. Cognitive entanglement refers to a relation in which human and AI activity become mutually shaping, irreducible to either party alone and organized across different developmental levels. The framework examines how repeated interaction with AI changes the ways users formulate problems, evaluate reasons, and make judgments. Unlike theories that locate the boundaries of cognition (the extended mind, enactivism) or explain the mechanisms of consciousness (global workspace, higher-order, predictive-processing, and integrated-information theories), cognitive entanglement examines whether sustained AI use preserves, weakens, or reorganizes users’ cognitive capacities. The article argues that current AI systems are often optimized for fluency, immediacy, and user satisfaction, and this may reduce the productive difficulty that supports higher-order cognitive development. If AI is to support human cognitive growth, design must move beyond answer provision and efficiency maximization toward the organization of productive human-AI relations: relations that challenge users’ initial assumptions while providing support appropriate to the task and the user’s level of expertise. The argument draws on philosophy of mind, cognitive science, and learning science, and compares divergent approaches to coupling in order to specify which forms of relation carry which developmental consequences. The concept shifts attention from AI as a tool or automation system to the developmental consequences of sustained human-AI interaction. Full article

(This article belongs to the Topic Learning to Live with Gen-AI)

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29 pages, 2632 KB  

Open AccessArticle

AI-Based Framework for Arabic Language Proficiency Assessment: A Deep Learning ASR Model with Enhanced Similarity Measures

by Sufian A. Badawi, Maen Takruri, Khouloud Salameh, Mohammad Al-Badawi, Nowar Alani, Isam ElBadawi, Aws Al-Qaisi and Ghaleb Aldoboni

Future Internet 2026, 18(5), 251; https://doi.org/10.3390/fi18050251 - 9 May 2026

Viewed by 803

Abstract

This work presents an innovative approach to test the Arabic language proficiency assessment via Automatic Speech Recognition (ASR) by enhancing the proficiency of the Whisper model in transcribing Arabic speech. The core of our research involved fine-tuning the Whisper model using a substantial, [...] Read more.

This work presents an innovative approach to test the Arabic language proficiency assessment via Automatic Speech Recognition (ASR) by enhancing the proficiency of the Whisper model in transcribing Arabic speech. The core of our research involved fine-tuning the Whisper model using a substantial, large-scale Arabic speech corpus, with a specific focus on Modern Standard Arabic. This process used a 2000-h Arabic-labeled speech corpus, the QASR dataset, and improved the model’s Word Error Rate (WER). After optimization, the fine-tuned Whisper model’s WER was reduced from 35% to 7% on the QASR dataset, corresponding to an absolute reduction of 28 percentage points (approximately 80% relative reduction). These results demonstrate the strong generalization ability of the fine-tuned model across multiple Arabic ASR benchmarks. A key component of our methodology was the development of a sophisticated scoring system. This system integrates various similarity metrics, such as cosine similarity, the Jaccard index, and the Levenshtein distance, with a machine learning regression model. This multifaceted system provides a comprehensive assessment of reading proficiency, proposing a practical automated assessment method that contributes to the field of AI language transcription and to its application in the assessment of students’ reading. Our research also introduces the ICONET dataset, an augmented Arabic speech corpus comprising 3160 h of diverse and tailored audio–text pairs designed for fine-tuning ASR models. This study demonstrates the potential of fine-tuning pretrained models for specific linguistic contexts (Arabic), establishing a foundation for future research in ASR and language technology. Full article

(This article belongs to the Topic Learning to Live with Gen-AI)

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15 pages, 1076 KB  

Open AccessPerspective

The Illusion of Causality in LLMs: A Developmentally Grounded Analysis of Semantic Scaffolding and Benchmark–Capability Mismatches

by Daisuke Akiba

Mach. Learn. Knowl. Extr. 2026, 8(3), 57; https://doi.org/10.3390/make8030057 - 2 Mar 2026

Viewed by 2607

Abstract

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that [...] Read more.

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal micro-worlds. Identical numerical evidence was presented to a LLM under two conditions: semantically meaningful variable labels and non-semantic coded labels. Across paired cases, the model reliably selected the correct causal structure when labels were meaningful, but frequently misidentified or exhibited instability in causal model selection under coded labels, despite producing locally coherent explanations. These divergences emerged most clearly in diagnostically ambiguous settings requiring suppression of misleading marginal associations. The results align with the claim that semantic scaffolding can support and stabilize apparent causal competence in LLMs without implying structure-sensitive reasoning. Full article

(This article belongs to the Topic Learning to Live with Gen-AI)

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18 pages, 939 KB  

Open AccessArticle

CPG-EVAL: Evaluating the Readiness of Large Language Models as Assistants and Teammates in Language Teaching

by Dong Wang

Informatics 2026, 13(2), 29; https://doi.org/10.3390/informatics13020029 - 6 Feb 2026

Cited by 1 | Viewed by 1658

Abstract

Large language models (LLMs) have begun to function as assistants or teammates in language learning, teaching, and research. However, what prerequisites are required for LLMs to reliably play these roles, and how such prerequisites should be measured, remains under-discussed. This study focuses on [...] Read more.

Large language models (LLMs) have begun to function as assistants or teammates in language learning, teaching, and research. However, what prerequisites are required for LLMs to reliably play these roles, and how such prerequisites should be measured, remains under-discussed. This study focuses on measuring Pedagogical Grammar Pattern Recognition (P-GPR) and establishes the Chinese Pedagogical Grammar Evaluation (CPG-EVAL), a multi-tiered benchmark designed to evaluate P-GPR within International Chinese Language Education. CPG-EVAL operationalizes grammar–instance correspondence through five task types that progressively increase contextual load and interference. We evaluate multiple proprietary and open-source LLMs as well as human participants. Results show a monotonic ordering across groups (humans > larger-scale models > semi-larger-scale models > smaller-scale models). In comparison with human participants, LLM performance is more sensitive to task-format complexity. In addition, we identify a set of completely failed items that consistently mislead all evaluated LLMs, exposing shared and systematic weaknesses in current models’ pedagogical grammar recognition. Overall, this study provides an operational framework for diagnosing the capabilities and risks of LLMs when they are deployed as assistants or teammates in grammar-related language-education tasks and offers empirical reference for safer and more syllabus-aligned use of LLMs in educational settings. Full article

(This article belongs to the Topic Learning to Live with Gen-AI)

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