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1

Wirtualna strzelnica – nowoczesne narzędzie edukacyjne i treningowe

Pietras Fabian, Streich Dominik, Sobczak Paweł, Sobczak Jarosław

Przegląd Techniczny : Gazeta Inżynierska

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2026

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nr 1

21--26

PL

W artykule przedstawiono innowacyjne stanowisko dydaktyczno-treningowe w postaci wirtualnej strzelnicy, opracowanej z wykorzystaniem technologii sztucznej inteligencji, analizy obrazu (OpenCV) oraz platformy Raspberry Pi. System pozwala na realistyczne i bezpieczne szkolenie w zakresie strzelectwa bez użycia amunicji ostrej. Projekt łączy w sobie edukację obronną, technologię informatyczną oraz symulację w środowisku 3D opartą o silnik Unity. W publikacji opisano budowę, zasadę działania, oprogramowanie oraz możliwości dydaktyczne opracowanego rozwiązania.

EN

The article presents an innovative didactic and training system in the form of a virtual shooting range, developed using artificial intelligence technologies, image analysis (OpenCV), and the Raspberry Pi platform. The system enables realistic and safe shooting training without the use of live ammunition. The project combines defense education, information technology, and 3D simulation based on the Unity engine. The publication describes the construction, operating principles, software, and educational applications of the developed solution.

2

Development and evaluation of a Yolo algorithm-based robotic sprayer for real-time weed detection

Al-Ahmadi Ameer H., Subr Alaa, Parafiniuk Stanisław, Milanowski Marek

Agricultural Engineering

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2026

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Vol. 30, No. 1

1--17

EN

Weed control with chemicals is a challenging process that should be performed in a rational way to reduce their negative impact on the surrounding environment. The growth of artificial intelligence algorithms encourages researchers to develop smart spraying robots that detect and spray weeds and distinguish them from the main crop which leads to sustainable use of these chemicals and achieves some of the sustainable development goals. However, few studies are available to comprehen-sively compare different versions of YOLO algorithm to detect weed. In this research, seven versions of YOLO algorithms were evaluated for their performance to detect and spray four types of weeds, namely, Cultivated licorice (Glycyrrhiza glabra L.), Dyer's Croton (Chrozophora verbascifolia), Lambsquarters (Chenopodium album L.), and Puncturevine (Tribulus terrestris L.) using a locally manufactured remotely controlled spraying robot. The results showed that YOLO v6n surpassed other algorithms which achieved the highest precision (0.89), recall (0.80), F1-score (0.84), mAp@0.50 (0.86), inference speed (18.83 fps), in addition to the field indicators including true positive rate (0.83), false negative rate (0.17), false positive rate (0.19), true negative rate (0.81).

PL

Zwalczanie chwastów za pomocą środków chemicznych to trudny proces, który należy przeprowadzać w sposób racjonalny, aby ograniczyć negatywny wpływ tych środków na otaczające środowisko. Rozwój algorytmów sztucznej inteligencji zachęca naukowców do tworzenia inteligentnych robotów do opryskiwania, które wykrywają i opryskują chwasty, odróżniając je od głównych upraw. Dzięki temu możliwe jest zrównoważone stosowanie chemikaliów i osiągnięcie niektórych celów zrównoważonego rozwoju. Jednakże dostępnych jest niewiele badań pozwalających na kompleksowe porównanie różnych wersji algorytmu YOLO do wykrywania chwastów. W niniejszym badaniu oceniono siedem wersji algorytmów YOLO pod kątem ich skuteczności w wykrywaniu i opryskiwaniu czterech rodzajów chwastów, a mianowicie lukrecji gładkiej (Glycyrrhiza glabra L.), krotonu barwierskiego (Chrozophora verbascifolia), komosy białej (Chenopodium album L.) i buzdyganka naziemnego (Tribulus terrestris L.), za pomocą zdalnie sterowanego robota opryskowego lokalnej produkcji. Wyniki pokazały, że YOLOv6n przewyższył inne algorytmy, które osiągnęły najwyższą precyzję (0,89), odwołanie (0,80), wynik F1 (0,84), mAp@0,50 (0,86), szybkość wnioskowania (18,83 fps), a także wskaźniki terenowe, w tym wskaźnik wyników prawdziwie dodatnich (0,83), wskaźnik wyników fałszywie ujemnych (0,17), wskaźnik wyników fałszywie dodatnich (0,19) i wskaźnik wyników prawdziwie ujemnych (0,81).

3

The impact of AI on online crisis counselling education: a case study from Ukraine

Bondar Kateryna, Shestopalova Olena, Bilosir Olga, Hamaniuk Vita

Zeszyty Naukowe Wyższej Szkoły Technicznej w Katowicach

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2025

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T. 20

129--145

EN

The article explores the convergence of human intelligence with artificial intelligence, emphasizing its potential to enhance education in the realm of mental health. This synergy is especially crucial in Ukraine, particularly within its educational institutions, following the pandemic and amid wartime conditions. The article delves into the concepts of ”digital mental health” and ”e-mental health,” shedding light on the significance of ”mental health technology” and ”digital mental health.” It also examines the standards for university courses in Mental Health Technologies and introduces a variety of Mental Health Apps, encompassing apps, wearables, platforms, data analytics resources, and other tools. The text underscores the importance of integrating artificial intelligence into both the education and economic sectors. It provides a comprehensive account of an experiment integrated into a standard university curriculum, involving Master’s psychology students at a pedagogical university. The results and conclusions of this experiment are thoroughly detailed. Moreover, the article investigates the impact of transactional distance on the learning experience of students pursuing Mental Health Technology courses in an online format at the Kryvyi Rih State Pedagogical University during the 2023-2024 academic year. Indicators of the transaction distance of the sample are researched and presented in detail. The influence of evaluation and its interaction on the level of transactional distance is analyzed as well. Applied logical and statistical tests were used, in particular the Pearson test for correlation analysis. The study findings affirm the critical role of synergizing human and artificial intelligence in addressing pressing challenges, enhancing mental health education, honing data analysis skills, and shaping a brighter future for mental well-being.

PL

Artykuł bada zbieżność ludzkiej inteligencji ze sztuczną inteligencją, podkreślając jej potencjał w zakresie wspierania edukacji w obszarze zdrowia psychicznego. Ta synergia jest szczególnie istotna na Ukrainie, zwłaszcza w instytucjach edukacyjnych, po pandemii i w warunkach wojennych. Artykuł omawia pojęcia „cyfrowe zdrowie psychiczne” i „e-zdrowie psychiczne”, zwracając uwagę na znaczenie „technologii zdrowia psychicznego” i „cyfrowego zdrowia psychicznego”. Przedstawione są również standardy dotyczące kursów uniwersyteckich w zakresie technologii zdrowia psychicznego oraz szeroka gama aplikacji związanych ze zdrowiem psychicznym, w tym aplikacje mobilne, urządzenia wearables, platformy, narzędzia analizy danych i inne zasoby. Artykuł podkreśla znaczenie integracji sztucznej inteligencji zarówno w sektorze edukacyjnym, jak i gospodarczym. Zawiera szczegółowy opis eksperymentu włączonego do standardowego programu studiów magisterskich z psychologii na uniwersytecie pedagogicznym. Wyniki i wnioski z tego eksperymentu zostały dokła dnie przedstawione. Ponadto artykuł analizuje wpływ dystansu transakcyjnego na doświadczenia edukacyjne studentów uczących się technologii zdrowia psychicznego w formacie online na Państwowym Uniwersytecie Pedagogicznym w Krzywym Rogu w roku akademickim 2023–2024. Przeanalizowano i szczegółowo przedstawiono wskaźniki dystansu transakcyjnego w badanej próbie. Zbadano także wpływ oceny, satysfakcji i ich interakcji na poziom dystansu transakcyjnego. W analizie zastosowano testy logiczne i statystyczne, w szczególności test korelacji Pearsona. Wyniki badań potwierdzają kluczową rolę synergii ludzkiej i sztucznej inteligencji w rozwiązywaniu pilnych problemów, wspieraniu edukacji w zakresie zdrowia psychicznego, rozwijaniu umiejętności analizy danych oraz kształtowaniu lepszej przyszłości dla dobrostanu psychicznego.

4

AI is coming, czyli swobodne rozważania nad sztuczną inteligencją

Piórkowski Adam, Gajer Mirosław

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

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2025

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nr 32

39--52

PL

Niniejszy artykuł przedstawia swobodne rozważania nad sztuczną inteligencją w kontekście odbioru społecznego i pokładanych w niej nadziei. Prezentowane są różne aspekty, przede wszystkim dotyczące edukacji i nauki. W sposób nawiązujący do tradycji i popkultury wyjaśniono wybrane zagadnienia związane z działaniem sztucznych sieci neuronowych, ze szczególnym wskazaniem tego, co jest pomijane w dyskursie medialnym: braków i niedociągnięć ze strony tej technologii. To, co oferują obecnie istniejące systemy sztucznej inteligencji jest bardzo dalekie od tego, co mogłoby być dopiero ewentualnie postrzegane jako prawdziwa sztuczna inteligencja. W szczególności obecnie nie ma absolutnie żadnych szans, aby można było się spodziewać, że jakikolwiek system sztucznej inteligencji będzie w stanie udowodnić przykładowo hipotezę Riemanna. Podobnie istniejące obecnie systemy komputerowego przekładu są również dalekie od pożądanego w tym zakresie ideału, a samo zastosowane w ich przypadku uczenie maszynowe nie jest bynajmniej w stanie rozwiązać skutecznie wszelkich pojawiających się tutaj problemów.

EN

This article presents free considerations on Artificial Intelligence in the context of social reception and hopes placed in it. Various aspects are presented, primarily those related to education and science. In a way referring to tradition and pop culture, selected issues related to the operation of Artificial Neural Networks are explained, with particular emphasis on what is omitted in media discourse, i.e. the shortcomings and deficiencies of this technology. Certainly, what is offered by currently existing artificial intelligence systems is still very far from what could possibly be seen as true artificial intelligence. In particular, there is currently absolutely no chance that any artificial intelligence system could be expected to be able to prove the Riemann hypothesis, for example, especially since this has been an open mathematical problem for more than 150 years, the solution of which is probably beyond the capacity of the human intellect. Similarly, the computer translation systems that currently exist are also far from the desired ideal in this respect, and the machine learning applied to them alone is by no means capable of effectively solving all the problems that arise in such systems.

5

The use of deep learning for military vehicle identification in SAR imagery

Ślesicka Anna, Ślesicki Błażej

Zeszyty Naukowe. Transport / Politechnika Śląska

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2025

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z. 128

251--268

EN

Artificial intelligence approaches, especially those involving deep learning, have recently become integral to object detection, as they can autonomously identify relevant features in visual datasets. The identification of military equipment, including mechanized vehicles, is crucial for threat detection and minimizing the impact of enemy actions by enabling countermeasures to be taken as quickly as possible after the threat is detected. The application of deep learning, particularly convolutional neural networks (CNN), is a highly effective tool for image processing and pattern recognition in visual data. These networks utilize convolutional layers to automatically extract features from images, making them ideal for analyzing synthetic aperture radar (SAR) imagery. Active sensor technologies like SAR are essential for object recognition due to their capability to operate in all weather conditions, both day and night.

6

Will social learning be hindered by Artificial Intelligence when the Lean Six Sigma management conception is implemented?

Świtek Sławomir

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

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2025

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Nr 91

153--171

EN

The increasing integration of Artificial Intelligence (AI) within Lean Six Sigma management practices raises critical questions about its impact on employee learning and collaboration. This study investigates whether AI disrupts traditional, experience-based, socially mediated learning or if it functions as a complementary tool that enhances continuous improvement. Three objectives guide the research: 1) to evaluate key adult learning theories – Kolb’s experiential cycle, Mezirow’s transformative learning, and Bandura’s social learning – in the context of Lean Six Sigma initiatives; 2) to analyse AI learning mechanisms, including deep learning, backpropagation, and reinforcement learning from human feedback (RLHF), comparing them to human social learning processes; and 3) to determine the potential for a symbiotic relationship between human and AI driven learning. A mixed method approach combines a systematic literature review via ResearchRabbit with the author’s two decades of Lean Six Sigma experience and a comparative analysis framework. The conceptual analysis suggests that AI has the potential to support reflective learning, simulate expert behaviour patterns, and facilitate knowledge consolidation. Importantly, these enhancements may occur without disrupting the critical reflection or collaboration essential to human social learning. The proposed conceptual framework for hybrid human – AI learning environments demonstrates that AI integration preserves essential social learning stages while offering data-driven insights. These results provide practitioners with evidence-based guidance for designing AI-augmented Lean Six Sigma programmes and suggest avenues for longitudinal field studies on hybrid learning outcomes.

PL

Rosnąca integracja sztucznej inteligencji (AI) w koncepcji zarządzania Lean Six Sigma rodzi istotne pytania o wpływ tej technologii na procesy społecznego uczenia się pracowników. Badanie analizuje, czy AI zakłóca tradycyjne, oparte na doświadczeniu i wymianie społecznej uczenie się, czy też staje się narzędziem komplementarnym, wspierającym ciągłe doskonalenie. Praca realizuje trzy cele: 1) ocenę kluczowych teorii uczenia się dorosłych – cyklu doświadczalnego Kolba, uczenia transformacyjnego Mezirowa i społecznego uczenia się Bandury – w kontekście inicjatyw Lean Six Sigma; 2) analizę mechanizmów uczenia się AI, w tym deep learning, backpropagation oraz reinforcement learning from human feedback (RLHF), w porównaniu z procesami społecznego uczenia się ludzi; 3) określenie potencjału symbiotycznej relacji między ludzkim i napędzanym przez AI uczeniem się. Wykorzystano podejście mieszane, łącząc przegląd systematyczny literatury za pomocą ResearchRabbit, dwudziestoletnie doświadczenie autora w Lean Six Sigma oraz analizę porównawczą. Analiza koncepcyjna sugeruje, że AI może wspierać uczenie refleksyjne, symulować wzorce zachowań ekspertów oraz ułatwiać konsolidację wiedzy. Co istotne, korzyści te mogą być osiągane bez zakłócania krytycznej refleksji czy współpracy charakterystycznej dla ludzkiego uczenia się społecznego. Zaproponowano ramy koncepcyjne dla hybrydowych środowisk uczenia się człowiek–AI, wykazując, że integracja AI zachowuje kluczowe etapy społecznego uczenia się, jednocześnie dostarczając wglądu opartego na danych. Wyniki te dostarczają praktykom wytycznych opartych na dowodach do projektowania programów Lean Six Sigma z AI oraz wskazują kierunki długofalowych badań terenowych nad wynikami hybrydowego uczenia się.

7

Using artificial intelligence in sustainable manufacturing – a case study of Northwood Pallets Producer LLC

Włodarczyk Katarzyna, Górska Agata

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

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2025

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Nr 92

23--35

EN

The article focuses on one of the modern technological tools – artificial intelligence (AI) and its use in an enterprise that operates in accordance with the principles of sustainable development. The aim of the study is to assess the use of artificial intelligence in the operations of a manufacturing company. The article is based on the experience of the Polish company Northwood Pallets Producer LLC (a manufacturer of wooden packaging). A comparison of three key areas shows the synergistic impact of AI on the production process at Northwood, visible from material analysis, through thermal protection of the product, to energy control. The company has not only implemented AI, but built it into its sustainable strategy, strengthening the existing pillars of its operations, i.e. through increased material efficiency, a focus on more favourable low-emission indicators, energy savings, reduced operating costs, and improved product quality, not only by introducing new solutions, but also by including these solutions in the product description and. Thus, the example of Northwood Pallets Producer LLC shows how integrating artificial intelligence in sustainable production processes can lead to improvements in operational efficiency, cost reductions and support for the sustainable development strategy.

PL

Artykuł koncentruje się na jednym z nowoczesnych narzędzi technologicznych – sztucznej inteligencji (AI) – oraz jej wykorzystaniu w przedsiębiorstwie działającym zgodnie z zasadami zrównoważonego rozwoju. Celem badania jest ocena wykorzystania AI w działalności przedsiębiorstwa produkcyjnego. Publikacja została opracowana na podstawie doświadczeń polskiej firmy Northwood Pallets Producer sp. z o.o. (producenta opakowań drewnianych). Porównanie trzech kluczowych obszarów pokazuje wpływ AI na proces produkcyjny w Northwood, widoczny od analizy materiałowej, poprzez ochronę termiczną produktu, po kontrolę energetyczną. Firma nie tylko wdrożyła AI, ale także wbudowała ją w zrównoważoną strategię, wzmacniając dotychczasowe filary swojej działalności, tj.: zwiększoną efektywność materiałową, skoncentrowanie się na korzystniejszych wskaźnikach niskoemisyjnych, zaoszczędzoną energię, poprawę jakości produktu nie tylko poprzez wprowadzenie nowych rozwiązań, ale również poprzez uwzględnienie zastosowanych rozwiązań w opisie produktu oraz obniżenie kosztów operacyjnych. Tak więc przykład Northwood Pallets Producer sp. z o.o. pokazuje, że integracja sztucznej inteligencji w zrównoważonych procesach produkcyjnych może prowadzić do poprawy efektywności operacyjnej, redukcji kosztów i wsparcia strategii zrównoważonego rozwoju.

8

On the factors of resistance to Artificial Intelligence in the workplace and ways to counter them

Jaworek Magdalena Anna

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

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2025

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Nr 91

173--185

EN

The implementation of new technologies in organizations constitutes a change that involves both new opportunities and threats, causing natural resistance among some employees to its introduction. The aim of this work is to present a model for overcoming resistance among employees to new digital technologies based on artificial intelligence. A critical literature analysis was used as the research method. The starting point for the model developed here is the latest work by Golgeci et al. (2025), presenting three resistance factors: affective (fear and aversion to new technologies) and cognitive (sense of ineffectiveness). Based on the research results and the psychological mechanisms behind the presented resistance factors, possible ways of overcoming it at the individual and organizational level were selected. The first group includes a positive change in attitude towards work and an increase in the level of identification with technologies based on artificial intelligence among their users. The second group includes a democratic management style and improving employee competences in terms of using artificial intelligence in the workplace. The model presented here is a preliminary proposal and can be supplemented with additional elements, both in terms of resistance factors and ways of counteracting them; at the same time, it can serve as a conceptualization for future research.

PL

Wdrażanie nowych technologii w organizacjach jest zmianą, z którą wiążą się zarówno nowe możliwości, jak i zagrożenia wywołujące naturalny opór części pracowników przed ich wprowadzaniem. Celem niniejszej pracy jest przedstawienie modelu przełamywania oporu wśród pracowników wobec nowych technologii cyfrowych opartych na sztucznej inteligencji. Zastosowaną metodą badawczą była krytyczna analiza literatury. Punktem wyjścia opracowanego modelu jest najnowsza praca Golgeciego i zespołu (2025) wskazująca trzy czynniki oporu: o charakterze afektywnym (strach i awersja do nowych technologii) oraz poznawczym (poczucie braku skuteczności). Na podstawie wyników badań oraz psychologicznych mechanizmów stojących za przedstawionymi czynnikami oporu dopasowano możliwe sposoby jego przełamywania na poziomie indywidualnym oraz organizacyjnym. Do pierwszej grupy zaliczono pozytywną zmianę postawy wobec pracy oraz zwiększenie poziomu utożsamiania się z technologiami opartymi na sztucznej inteligencji wśród ich użytkowników, do drugiej – demokratyczny styl zarządzania oraz podnoszenie kompetencji pracowników pod kątem wykorzystania sztucznej inteligencji w miejscu pracy. Przedstawiony model jest wstępną propozycją i może być uzupełniony o dodatkowe elementy, zarówno po stronie czynników oporu, jak i sposobów przeciwdziałania im; jednocześnie może służyć jako konceptualizacja przyszłych badań.

9

Generation Z and artificial intelligence – research directions in management science: a scientometric analysis based on Scopus

Ławińska Olga, Korombel Anna

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

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2025

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Nr 91

223--246

EN

The aim of the article is to identify current and emerging research directions in management science in the area of topics linking artificial intelligence and Generation Z. The article uses scientometric analysis. Publications included in the Scopus database combining the topics of artificial intelligence and Generation Z in the area of management sciences were analysed. VOSviewer software was used for scientometric analysis. The study covered the period from 2018 to 2025 (up to and including 9.04.2025). The most frequent keywords were „social media” and „technology acceptance model”. Through a combination of cluster analysis and critical review, five research themes were identified in each cluster: Gen Z’s intention to use a technology, influenced by perceived ease of use; Generation Z’s trust and satisfaction in the use of AI tools; the impact of using augmented reality, artificial intelligence-enabled chatbots, and social media on the purchasing behaviour of Generation Z; robot implementation during the COVID-19 pandemic, often focusing on comparing customer experiences across Generations X, Y and Z; the adaptation of metaverse and service robots technologies by Generation Z in a virtual reality. The authors also identified emerging research directions. The first of them is the use of psychology theory in understanding Generation Z’s attitudes towards AI (in different relationship spheres, e.g. work, school). The second is the study of Generation Z representatives’ relationships with other users in a virtual metaverse, understood as a digital space where users can interact with other users.

PL

Celem artykułu jest identyfikacja obecnych i wyłaniających się kierunków badań w naukach o zarządzaniu w obszarze tematyki łączącej sztuczną inteligencję i pokolenie Z. W pracy wykorzystano analizę scjentometryczną. Analizie zostały poddane publikacje zamieszczone w bazie danych Scopus łączące tematykę sztucznej inteligencji i pokolenia Z w obszarze nauk o zarządzaniu. Do analizy scientometrycznej wykorzystano oprogramowanie VOSviewer. Badaniem objęto okres od 2018 do 2025 r. (do 9.04.2025 włącznie). Najczęściej pojawiającymi się słowami kluczowymi były „media społecznościowe” i „model akceptacji technologii”. Dzięki połączeniu analizy klastrów i krytycznego przeglądu zidentyfikowano pięć tematów badawczych w poszczególnych klastrach: zamiar korzystania z technologii przez pokolenie Z pod wpływem postrzeganej łatwości użytkowania; zaufanie i satysfakcja pokolenia Z z korzystania z narzędzi sztucznej inteligencji; wpływ korzystania z rzeczywistości rozszerzonej, chatbotów obsługujących sztuczną inteligencję i mediów społecznościowych na zachowania zakupowe pokolenia Z; wdrażanie robotów podczas pandemii COVID-19, często koncentrujące się na porównywaniu doświadczeń klientów między pokoleniami X, Y i Z; adaptacja technologii metawersum i robotów usługowych przez pokolenie Z w wirtualnej rzeczywistości. Autorki wskazały także wyłaniające się kierunki badań. Pierwszym z nich jest wykorzystanie teorii psychologii w zrozumieniu postaw pokolenia Z względem sztucznej inteligencji (w różnych obszarach relacji, np. w pracy, szkole). Drugim natomiast jest badanie relacji przedstawicieli pokolenia Z z innymi użytkownikami w wirtualnej przestrzeni metawersum, rozumianej jako cyfrowa przestrzeń, w której użytkownicy mogą wchodzić w interakcje z innymi użytkownikami.

10

Artificial intelligence technologies in the management of higher education institutions: a scoping review

Tutko Marta Maria

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

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2025

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Nr 91

205--222

EN

The development of artificial intelligence (AI) technologies has brought unprecedented challenges and opportunities for higher education institutions (HEI). The possibilities of using AI technologies in specific areas of HEIs management need to be explored to keep up with the ongoing dynamic changes. For this reason, the objective of this study is to identify management levels and management subdisciplines in HEIs at which AI technologies are used. To achieve this aim, the scoping review method was chosen. Web of Science and Scopus databases were used to identify documents published 1992-2025, from which 11 publications were considered eligible for the review. Research shows that HEIs apply diverse AI technologies (e.g. machine learning, expert systems, chatbots). HEIs also utilize AI technologies at the operational level of management in the subdisciplines of quality management, knowledge management, and managerial decision support. In the case of functional level of management, human resources management and financial management and managerial accounting subdisciplines are mentioned in the sources. However, the findings also reveal that AI technologies are implemented in less than half of the identified subdisciplines. This suggests a limited and uneven adoption of AI technologies. Strikingly, the strategic level of management remains entirely absent from the reviewed literature. This gap might suggest that AI technologies are often deployed in a compartmentalized manner, rather than as part of an integrated institutional strategy. To fully harness AI’s transformative potential, HEIs should adopt a holistic approach that embeds AI technologies across all levels of management, namely strategic, operational, and functional.

PL

Rozwój technologii sztucznej inteligencji (SI) niesie ze sobą bezprecedensowe wyzwania, ale i liczne możliwości dla instytucji szkolnictwa wyższego (ISW). Aby nadążyć za dynamicznymi zmianami, konieczne jest systematyczne badanie potencjału zastosowania SI w różnych obszarach zarządzania uczelniami. Celem niniejszego badania jest identyfikacja poziomów oraz subdyscyplin zarządzania, w których technologie SI znajdują zastosowanie w szkołach wyższych. W celu realizacji tego założenia przyjęto metodę przeglądu zakresu literatury. Analizie poddano publikacje dostępne w bazach danych Web of Science i Scopus obejmujące lata 1992-2025. Do ostatecznego przeglądu zakwalifikowano 11 artykułów. Wyniki badania pokazują, że uczelnie stosują różnorodne technologie SI – takie jak uczenie maszynowe, systemy eksperckie czy chatboty – przede wszystkim na operacyjnym poziomie zarządzania, w subdyscyplinach takich jak zarządzanie jakością, zarządzanie wiedzą oraz wspomaganie decyzji kierowniczych. Na funkcjonalnym poziomie zarządzania najczęściej wykorzystywane są w subdyscyplinach zarządzania zasobami ludzkimi, zarządzania finansami i rachunkowości menedżerskiej. Wyniki wskazują, że technologie SI są stosowane w mniej niż połowie zidentyfikowanych subdyscyplin zarządzania. Świadczy to o ograniczonym i nierównomiernym wdrażaniu tych rozwiązań, a wiele potencjalnych obszarów nadal pozostaje niedostatecznie zbadanych. Co istotne, strategiczny poziom zarządzania nie pojawia się w żadnym z analizowanych dokumentów. Może to sugerować, że wdrożenia SI są często incydentalne i nieskoordynowane, zamiast stanowić element spójnej strategii instytucjonalnej. Aby w pełni wykorzystać transformacyjny potencjał sztucznej inteligencji, uczelnie wyższe powinny przyjąć holistyczne podejście obejmujące wszystkie poziomy zarządzania – strategiczny, operacyjny i funkcjonalny.

11

The use of artificial intelligence in project management

Łukasik-Stachowiak Katarzyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

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z. 217

281--300

EN

Purpose: The main purpose of the article was to indicate the possibilities of using artificial intelligence as a tool in project management processes in the studied medium-sized Polish companies. Design/methodology/approach: This pilot study utilized an online survey questionnaire, which was developed following a thorough review of existing literature. The research focused on two key questions: Q1: What impact does AI have on Project Management? Q2: How are AI tools evaluated by its users in Project Management? Findings: The implications arising from this research extend both theoretically and practically. Theoretically, it enriches the existing literature on AI in project management, underscoring the potential of AI to enhance project performance and decision-making. Practically, the findings offer valuable insights for project managers and organizations striving to integrate AI tools effectively, thereby improving efficiency, minimizing risks, and optimizing resource allocation. The research reveals that the integration of AI in project management significantly improves efficiency, decision-making, and risk management throughout the project lifecycle. Research limitations/implications: This study examining the application of AI in project management encounters limitations like: the fast-paced development of AI technologies poses a significant challenge in maintaining the relevance of research findings, AI research may lean heavily on case studies, surveys, or secondary data, which might not adequately reflect broader industry trends, and the effective implementation of AI in project management relies significantly on the skills, attitudes, and acceptance of project teams and managers. Practical implications: The application of AI-driven solutions enhances the execution of project management activities, leading to improved work efficiency and quicker results. It is anticipated that modern technology will be embraced with increasing boldness and innovation. Social implications: The growing use of AI in project management has significant social implications, particularly concerning the future of work, human-AI collaboration, and the ethical use of technology in organizational settings. Originality/value: This article explores the growing integration of AI-based solutions in project management. As businesses increasingly adopt AI, it is transforming operations by automating processes. This shift not only enhances efficiency but also improves the effectiveness of task execution, particularly for repetitive activities.

12

Service employees’ appraisals toward artificial intelligence, robotics and automation in the workplace - results of exploratory and confirmatory factor analyses

Bednarska Marlena A., Łuka Paweł

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

|

z. 219

69--88

EN

Purpose: In view of the rapid advancements in artificial intelligence, robotics, and automation (AIRA) within service industries, it is crucial to understand how these technologies are perceived by organizational members to ensure their effective implementation. Therefore, the purpose of this paper is to gain insights into employees’ appraisals toward AIRA in the workplace in service settings. Design/methodology/approach: A quantitative approach was adopted, with data being collected via a self-administered online survey from 369 service employees in Poland. The sample was randomly split into two subsamples. The first subsample was used for exploratory factor analysis (EFA), while the second subsample was used for confirmatory factor analysis (CFA). Findings: The results of the EFA and CFA indicate that the AIRA appraisal scale is multidimensional and consists of three subscales: resource appraisal toward AIRA, challenge appraisal toward AIRA, and hindrance appraisal toward AIRA. AIRA in the workplace is perceived by service employees predominantly as a resource, then as a challenge, and lastly as a hindrance. Research limitations/implications: The data collection was based on the non-random sampling technique and the questionnaire was disseminated among employees of selected service industries in Poland, which limits the generalizability of the findings beyond the specific context of this research. Originality/value: Drawing upon the refined job demands-resources (JD-R) model, the study provides a comprehensive and nuanced perspective on employees' perceptions of AIRA integration into service delivery processes. The proposed perspective is better suited to explain employees’ attitudinal and behavioral reactions to AIRA-driven changes in the work environment.

13

Implementation of artificial intelligence for employee onboarding in light of literature review

Jasiak-Kaczmarek Bożena

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

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z. 219

281--292

EN

Purpose: The purpose of this study was to review the latest reports and developments in the implementation of artificial intelligence (AI) in the professional adaptation of new employees based on literature research. Design/methodology/approach: The method of analysis and criticism of the literature was used. A search was performed according to accepted searches in scientific databases: Google Scholar, Scopus, Science Direct, and EBSCO. Scientific items were supplemented by industry literature and online sources treating issues within the scope of the subject matter. Findings: The potential of applying artificial intelligence to the professional adaptation of employees has not yet been thoroughly explored. The state of knowledge in artificial intelligence in onboarding is small, as is the number of literature items on the subject. Based on the analysed literature, it can be concluded that the cooperation of humans and artificial intelligence is indispensable in the HR department, as it has much potential for improving its processes. It facilitates the smooth transition of new employees from one company to another and faster socialisation. Modern technologies have changed onboarding processes, leading to their personalisation and, consequently, an increase in the engagement of newly hired employees and satisfaction with their work. Practical implications: Implementing artificial intelligence contributes to the automation of deployment processes, individualisation, and continuous monitoring of new employees' progress. Analysing the data derived from the adaptation process makes it possible to identify areas that require modification. Social implications: Socialization-oriented onboarding promotes good moods among new employees. Onboarding with elements of artificial intelligence makes hired individuals feel "taken care of," which reduces the stress of changing work environments and increases self-esteem. The employee adapts faster to the new work environment and identifies with the organisation's values. Originality/value: The article presents a comprehensive picture of onboarding practices using AI in the human resources department. Drawing on foreign literature on the subject enriches the existing body of research on human capital in the enterprise, signalling the author's contribution to developing the discipline of management and quality sciences. The article is aimed primarily at researchers and scholars working in the field of human resource management. In addition, the article is of value to HR managers and employees who are directly affected by implementing modern technologies in onboarding.

14

Artificial intelligence in territorial marketing

Ciechomski Wiesław

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

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z. 219

107--121

EN

Purpose: The purpose of this paper is to present a modern approach to the optimization of territorial marketing through discussing selected possibilities of applying artificial intelligence in the marketing management of territorial units such as regions, cities, districts, or municipalities. Design/methodology/approach: The paper is based on a review of the literature and online sources - 51 bibliographic items in total. The time scope of the conducted analysis covers the years 2014-2024. Additionally, in order to present a comprehensive picture of the current trends, the method of desk research was used to analyse existing empirical data and professional literature on the subject. The analysis made use of inductive reasoning, which makes it possible to draw conclusions from the observed processes. Findings: Artificial intelligence has enormous potential, capable of revolutionizing marketing management. Also, its potential and actual implementation in place marketing offers the possibility of obtaining a competitive advantage and extraordinary development opportunities. Combining creative economic ideas with the latest IT technologies makes it possible to achieve a synergistic effect in such areas as, for example, creating effective promotional campaigns for cities and regions. Research limitations/implications: In the future it would certainly seem worthwhile to conduct representative empirical studies among managers involved in the promotion of cities to diagnose the actual implementation of selected artificial intelligence tools by the local government units responsible for the creation and implementation of promotional campaigns addressed to residents, tourists and investors. The content of this article can be helpful in developing the methodology for such research. Practical implications: Effective management of the development strategy and promotion of territorial units plays a major role in the competition between cities for limited resources; which include funds from investors, tourists and local communities. Local government units which are managed in a modern way are open to new, innovative and creative ideas in their promotional media campaigns. Undoubtedly, the range of tools offered by artificial intelligence makes it easier to generate and implement original solutions that promote interactions with stakeholders as well as enabling effective monitoring of ongoing advertising campaigns. Originality/value: The findings have cognitive value. The article describes the role of artificial intelligence in territorial marketing and the instruments used in this area.

15

Artificial intelligence in student life – opportunities and threats

Radecka Katarzyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

|

z. 220

195--205

EN

Purpose: The aim of this article is to assess the role of artificial intelligence (AI) in the daily lives of students and to present the opportunities and threats it brings. Design/methodology/approach: The study analysed the literature on the development of artificial intelligence and its use in education. Compilations and reports on AI were analysed. The study was conducted by means of a Google survey distributed to students at the Bydgoszcz University of Technology. Findings: Artificial intelligence, while demonstrating human-like abilities such as learning, critical thinking and problem solving, but raises a variety of emotions. Currently, however, its role should focus on repetitive tasks, which would allow teachers to pay more attention to the individual needs of students. Unlike science disciplines such as mathematics, AI should focus primarily on supporting this aspect of education rather than replacing it. Research limitations/implications: Future research may be related to the creation of mentoring programmes in the area studied. Practical implications: The results of the study can be used as input for the design of training programmes in the study area. Social implications: Artificial intelligence (AI) in higher education presents huge opportunities. It helps to personalise learning, access knowledge faster and automate tasks. However, it also brings risks, such as plagiarism, dependence on technology, and reduced critical thinking skills. Appropriate use of AI is key. Originality/value This article is mainly addressed to education professionals who want to implement and correctly use artificial intelligence in the teaching process.

16

Artificial intelligence in manufacturing - systematic literature review

Tarasiuk Wojciech, Halicka Katarzyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

|

z. 215

677--701

EN

Purpose: The purpose of this paper is to explore the current state of research on artificial intelligence in manufacturing. The paper aims to identify key trends, leading authors, institutions and research topics, as well as to identify the main areas of scientific interest in this field. Design/methodology/approach: The research objectives were achieved by using a systematic literature review and bibliometric analysis. The study used Web of Science and Scopus databases, where searches were conducted according to specific keywords and inclusion criteria, such as document type, language and publication time range (2015-2024). The collected data was then analyzed for the distribution of documents by type, year of publication, country, institution, author, and co-occurrence of keywords, which made it possible to extract major thematic clusters and research trends. Findings: The analysis revealed five thematic clusters representing key research areas, alongside a rapid growth in publications from 2019, particularly in countries such as China, the United States, and India. These findings highlight an increasing global focus on AI's application in manufacturing. Originality/value: This article offers a comprehensive and up-to-date analysis of research on artificial intelligence in manufacturing, covering publications up to 2024. By identifying five key thematic clusters, it provides unique insights that will benefit researchers, industry practitioners, and decision-makers aiming to integrate AI into manufacturing processes. The study provides a better understanding of research trends and developments in the field, making it a valuable resource for researchers, industrial practitioners and decision makers interested in integrating AI into manufacturing processes.

17

Artificial intelligence in everyday life - practical applications

Gostkowska-Dźwig Sylwia, Mrozik Magdalena

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

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z. 218

237--249

EN

Purpose: Analysis of practical applications of artificial intelligence in the modern world and their impact on people's everyday functioning. Design/methodology/approach: In order to implement the assumptions of the paper, an approach based on literature analysis and survey research was used. The main research methods include: Literature studies - analysis of existing scientific publications, industry reports and articles on practical applications of AI in everyday life. Thanks to this, the main areas in which AI is used were identified, and the key benefits and challenges related to its use were identified. Survey - conducting research among respondents to collect data on the level of awareness and experiences related to AI in everyday life. The survey allows to assess the extent to which users use technologies based on AI and what their attitudes are towards them. The theoretical scope includes the definition of artificial intelligence, its key technologies and a review of literature on its applications. The thematic scope of the work focuses on practical aspects of the use of AI in various areas of life. The approach combines the analysis of existing knowledge with empirical research, which allows for a more complete picture of the impact of artificial intelligence on everyday life. Findings: During the work, it was found that artificial intelligence is playing an increasingly important role in everyday life, and its practical applications cover a wide range of fields. Based on the analysis of the literature and the survey results, it was found that AI is widely used and there are many benefits resulting from its use. However, despite many advantages, users also see potential risks. The analyses also show that many people use AI solutions. Research limitations/implications: Despite the cognitive value of the article and the research conducted, there are some limitations that may affect their results and interpretation. The survey was conducted on a specific group of respondents, which may not fully reflect the global approach to AI. The results may be conditioned by the cultural context, level of technological knowledge or age group of participants. The study focuses on selected aspects of the practical use of AI. AI is a technology that is developing rapidly, which means that the conclusions formulated in the article may require updating over time due to the emergence of new trends, tools and legal regulations. Although the article addresses issues related to privacy and ethics, it does not constitute a complete analysis of these issues. Future research could more thoroughly examine the impact of AI on user rights, legal regulations and ethical challenges related to process automation. Suggestions for future research include: expanding the study to a larger and more diverse group of respondents, which would allow for more representative results; analyzing the long-term impact of AI on everyday life, taking into account forecasts and future technology trends; more closely examining the ethical and legal issues related to the development of AI; and comparing the perception of AI across different social and professional groups to determine what factors influence the level of acceptance of this technology. Practical implications: The research results and literature analysis indicate that artificial intelligence has a significant impact on everyday life, which carries significant consequences for business, economy and social practice. The main practical implications include optimization of business processes, personalization of services and products, increasing accessibility and convenience, changes in the labor market, impact on economies and trade. Social implications: Research on artificial intelligence and its practical applications in everyday life has a wide impact on society, shaping both social attitudes and approaches to technology. Key social consequences resulting from the analysis of AI include: changes in social attitudes towards technology, impact on employment and the labor market, social and ethical responsibility, impact on public policy and regulations, quality of life and social well-being Originality/value: The article makes a significant contribution to understanding the practical applications of AI in everyday life, highlighting both the benefits and challenges of its implementation. It highlights both the benefits and potential risks of the growing role of AI.

18

Artifical Inteligence in HR processes

Smarżewska Daria

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

|

z. 217

523--536

EN

Purpose: The purpose of the publication was to identify and assess the potential and actual state of artificial intelligence (AI) tools used in HR processes within large and medium-sized enterprises. Design/methodology/approach: The research problems were formulated as the following questions: 1) In which HR processes do enterprises utilize artificial intelligence? 2) What are the primary barriers associated with the use (or lack thereof) of artificial intelligence in HR processes within enterprises? The research employed a qualitative method-individual in-depth interviews (IDIs). The sample was purposefully selected and included employees, managers, and HR department directors. Data collection was conducted using a custom-designed interview guide. Findings: The study revealed that AI is applied in processes such as recruitment, report generation, administrative task automation, and content creation. The benefits of AI implementation primarily include time savings and improved quality of analyses. However, a significant portion of respondents does not use AI due to barriers such as high implementation costs, lack of competencies, and concerns regarding data security. Employee resistance to change and automation also represents a significant challenge. Research limitations/implications: The study covers large and medium-sized enterprises. Future research should involve quantitative analyses and examine industry- and sector-specific differences in AI implementation within HR processes. Practical implications: The article can serve as a source of knowledge for managers and HR department staff, helping them identify processes where AI can deliver the greatest benefits. The research findings can support the planning of AI implementation strategies and the mitigation of technological and organizational barriers. Social implications: The implementation of AI in HR processes can influence employees' quality of life by automating repetitive tasks, thereby allowing a focus on more valuable and creative activities. At the same time, concerns related to data privacy and the risk of job displacement due to automation should be considered. Originality/value: The article presents original research on the application of artificial intelligence in HR processes. It addresses critical issues related to the benefits, barriers, and future directions for AI implementation in human resource management.

19

AI-based tools’ impact on university education

Kwiatkowska Kamila, Zakrzewska Małgorzata, Zsigmond Tibor

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2025

|

z. 218

399--409

EN

Purpose: The main reason for writing the paper was to present the latest research studies on using AI in education and present the survey studies on students' opinions according to AI-based tools in learning process. Design/methodology/approach: The theoretical part of the article presents research from the last 5 years on AI in education and higher education. The empirical part presents the results of surveys conducted among students of the University of Economics in Krakow on their opinions on the impact of AI-based tools on their learning process. Findings: The research show that the vast majority (95.1%) of respondents see that tools based on AI facilitate the learning process and provide valuable didactic support. Despite positive assessments, respondents express concerns about credibility, privacy and potential addiction to technology. Research limitations/implications: The results suggest the need for appropriate regulation and education regarding the use of AI-based tools. The study is limited by the too rapid development of AI in recent times and the ever-increasing number of new tools used in the student learning process. Practical implications: The study revealed that 95.1% of students find AI tools like ChatGPT, Canva, and Quizlet beneficial for learning, although concerns about credibility, privacy, and dependency remain. It suggests universities should implement AI tools and train staff in their use while addressing risks and ensuring equal access for all students. Social implications: By highlighting the benefits of AI in education, the study may foster more positive public attitudes towards technological integration in learning environments. With AI tools enhancing the learning experience and potentially improving educational outcomes, students may enjoy improved academic success and career prospects, ultimately contributing to a higher quality of life. Originality/value: The study is notable for its focus on students and their subjective assessments of the opportunities and concerns related to the use of AI, especially in the context of tools such as ChatGPT, Canva, and Quizlet, which sheds light on their growing importance and challenges in higher education.

20

Rozmowy z AI o zasadach stanowienia taryf - ciąg dlaszy

Bylka Henryk, Bylka Jędrzej

Wodociągi - Kanalizacja

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2025

|

nr 4

12--16