Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T21:16:25.795Z Has data issue: false hasContentIssue false

Use of Open Access AI in teaching classical antiquity. A methodological proposal

Published online by Cambridge University Press:  03 November 2023

Carlos Díaz-Sánchez*
Affiliation:
Department of Prehistory, Ancient History and Archaeology, Universidad Complutense de Madrid, Madrid, Spain
Diego Chapinal-Heras
Affiliation:
Department of Ancient and Medieval History, Paleography and Diplomacy, Universidad Autónoma de Madrid, Madrid, Spain
*
Corresponding author: Carlos Díaz-Sánchez; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

The aim of this contribution is to present an innovative approach to the use of Open Access AI in teaching the Classical era at high school and university level. The paper first explains the growing interest in AI technology and its main applications in the subjects of philology, history and other related areas. The following sections show the different steps of the proposal, which uses the Midjourney program, as well as its pros and cons.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The Classical Association

Introduction

New technologies are transforming the way the classical era is taught. A good example is the use of virtualisation and 3D modelling of different archaeological sites and artifacts, thus bringing to the classroom materials that are often unreachable in person (Chapinal et al., Reference Chapinal-Heras, Rey-Álvarez, Díaz-Sánchez, Pagola-Sánchez, Gómez-García, España-Chamorro, Bantim de Assumpçâo, Fornis, da Costa Campos and Ferreira Monteiro2023a, Reference Chapinal-Heras, Díaz-Sánchez, España-Chamorro, Gómez-García, Pagola-Sánchez, Parada López De Corselas and Zafiria2023b). There are however very few cases of the use of artificial intelligence (AI) in high school and university teaching, although there are some interesting approaches in learning new languages (Nasimovna, Reference Nasimovna2022; Ross, Reference Ross2023; Toksha et al., Reference Toksha, Kulkarni, Gupta, Churi, Joshi, Elhoseny and Omrane2022) and methodologies with e-Learning (Fahimirad, Reference Fahimirad2018; Garg and Sharma, Reference Garg and Sharma2020; Guan et al., Reference Guan, Mou and Jiang2020; Jia and Zhang, Reference Jia and Zhang2021).Footnote 1

The application of AI in History and Archaeology, specifically in Classical Antiquity, is very rare (Chapinal-Heras and Díaz-Sánchez, Reference Chapinal-Heras and Díaz-Sánchez2023). The usefulness of AI has been largely proved in other areas, such as the health sciences (Vaishya et al., Reference Vaishya2020), business (Jarrahi, Reference Jarrahi2018; Makridakis, Reference Makridakis2017), and virtual environments (Kolve et al., Reference Kolve, Mottaghi, Han, Vander, Weihs, Herrasti, Deitke, Ehsani, Gordon, Zhu, Kembhavi, Gupta and Farhadi2022). AI is seen as the future, the core of a new ‘industrial revolution’ (Makridakis, Reference Makridakis2017). This potential is based on AI's ability to interpret external data accurately, automatically learning from them and applying that knowledge to specific tasks (Bengio, Reference Bengio2009; García-Serrano and Menta Garuz, Reference García-Serrano and Menta Garuz2022). This self-learning process, known as deep learning, is one of the most usual mechanisms implemented in AI algorithms. The aim is to enable the ‘machine’ to think and learn as if it were human (Bengio et al., Reference Bengio, Lecun and Hinton2021).

In the Humanities, AI is a useful resource for researchers who need to analyse data within the context of Big Data and Natural Language Processing (NLP) (García Serrano and Menta Garuz, Reference García-Serrano and Menta Garuz2022). We can observe some examples in literature, where AI is trained to examine and compare discourse or interpretative frameworks (Cuéllar and Vega García-Luengos, Reference Cuéllar and Vega García-Luengos2023; DiMaggio et al., Reference DiMaggio, Nag and Blei2013; Köntges, Reference Köntges2020; Schöch, Reference Schöch2017). Another area is the study of calligraphic ductus, in which computerised palaeography enables a stylometric analysis to be carried out using algorithms. This is very useful when attempting to identify handwriting in fields such as palaeography, epigraphy and diplomacy (Azmi et al., Reference Azmi, Omar, Nasrudin, Muda and Abdullah2011; Cuéllar, Reference Cuéllar2023; Sommerschield et al., Reference Sommerschield, Assael, Pavlopoulos, Stefanak, Senior, Dyer, Bodel, Prag, Androutsopoulos and Freitas2023; Wolf et al., Reference Wolf, Potikha, Dershowitz, Shweka and Choueka2011). It can even transcribe documents, examining and comparing them to recognise their authorship (Kang, Reference Kang2021; Meza-Lovn, Reference Meza-Lovn2012; Tuzzi and Cortelazzo, Reference Tuzzi and Cortelazzo2018). AI has been trained in different projects to aid researchers to process and interpret texts in Latin and Greek. Examples include APELLO (Roued-Cunliffe, Reference Roued-Cunliffe2010) and EAGLE (Amato et al., Reference Amato, Falchi and Vadicamo2016) aimed at identifying letter-cutters in Athenian inscriptions (Panagopoulos et al., Reference Panagopoulos, Papaodysseus, Roussopoulos, Dafi and Tracy2009; Tracy et al., Reference Tracy, Papaodysseus, Roussopoulos, Panagopoulos, Fragoulis, Dafi and Panagopoulos2007), an attempt to decrypt Mycenaean Linear B and the Ugaritic script (Luo et al., Reference Luo, Cao and Barzilay2019), or even to partially or totally restore words and sentences through pattern recognition (Assael et al., Reference Assael, Sommerschield, Shillingford, Bordbar, Pavlopoulos, Chatzipanagiotou, Androutsopoulos, Prag and de Freitas2020; Ross, Reference Ross2023). In addition, archaeological research has implemented several algorithms that have enabled programs to learn to identify settlement patterns, buildings and artifacts discovered through telematic prospections and drone flights (Argyrou and Agapiou, Reference Argyrou and Agapiou2022; Berganzo-Besga et al., Reference Berganzo-Besga, Orengo, Lumbreras, Carrero-Pazos, Fonte and Vilas-Estévez2021; Caspari and Crespo, Reference Caspari and Crespo2019; Davis et al., Reference Davis, Gaspari, Lipo and Sanger2021; Orengo and Garcia-Molsosa, Reference Orengo and Garcia-Molsosa2019).

Despite the most recent advances in AI in the Digital Humanities, this is neither an easy nor a common practice. Each project develops its own AI approach and trains it with a different software, although they usually function in a similar way to each other. The design of these AI algorithms, together with the creation or acquisition of the Big Data required to train it, is very expensive. Not many research groups, projects or scholars can afford to create their own software. For this reason, in this paper we present an activity using Open Access AI, a free methodology that can contribute to a new use of virtual resources in the high school and university teaching of the Classical era.

Materials and methods

Our methodological approach consists of the application of Open Access AI to improve and strengthen the historical, archaeological and philological knowledge of classical antiquity. Students of this field can acquire and develop new skills to better understand and analyse literary sources. A frequent issue perceived in teaching is a lack of comprehension, synthesis and imagination to process the abstract concepts, landscapes and mythological figures that appear in the ancient accounts. The use of a resource that allows an image to be generated on the basis of a textual description can be very useful in helping students to improve their skills by means of self-assessment.

Midjourney is the Open Access AI chosen for this teaching practice.Footnote 2 This software was developed by the autonomous team led by D. Holz and operates with the Discord application.Footnote 3 By installing a bot,Footnote 4 a person can interact using different commands. To begin with, it is necessary to enter the ‘/imagine prompt:’ command and then insert the textual description to enable the program to generate an image related to that reference. The text is processed and transformed into a transliteration that enables the algorithm to learn to interpret it and create an image. The more it is trained in this task, the more it learns (NLP) (Pagé-Perron et al., Reference Pagé-Perron, Sukhareva, Khait, Chiarcos, Beatrice, Degaetano-Ortlieb, Kazantseva, Reiter and Szpakowiz2017). Our approach focuses on this method, using AI algorithms we believe can be of benefit to teaching, in place of the traditional master class.

Approach

Our activity focuses on the ability to examine a topic mentioned in the ancient sources to work with the AI in relevant aspects within high school and university studies of classical antiquity. In this process, which is conceptualised in Figure 1, the teacher or professor presents the subject, which can be a landscape, a beast or a real or mythological artifact. The students have to search for information in the ancient literary sources. By undertaking this research, they gain more practice in dealing with the different available sources.Footnote 5

Figure 1. Methodological approach. Stages and possibilities of the activity.

Once the passages have been selected, the students are required to translate them from the original Greek or Latin. In this way, they apply their knowledge of Greco-Latin vocabulary and grammar. If two or more students work with the same account, it is likely that each will present a unique translation, different from the others, with possible conceptual variations. Likewise, many of the translated passages are long and may contain rhetorical figures, such as sarcasm, alliterations or ironic references. These need to be transformed into more logical descriptions, since AI has issues when dealing with abstract texts. It is therefore crucial that the students fully understand the text they have translated, assimilated and synthesised, in order to be able to process the information it contains and thus offer a depiction that AI can process.

In the next step, the students have to access Midjourney and enter the ‘/imagine [Prompt: ]’command followed by their text. Based on this depiction, the AI will generate four images that contain the elements mentioned in the account. It is probable that the program will not depict certain specific details in the images, meaning the student will need to modify the description until Midjourney creates a more accurate depiction. This type of self-assessment allows the student to undertake a second translation (and others if necessary), seeking a more specific vocabulary to better define their study case.

When the final image is obtained, the students can show their work to their classmates, explaining the main features of the depictions, how they correspond to the textual description and the sources consulted to create it. This step encourages active participation in the classroom and leads to a better comprehension of the study case. The illustration generated by the AI enables a discussion as to whether it is reliable or not, based on its similitude to the literary reference, as well as whether the iconography used in the image is accurate.

Finally, the students submit their work to the teacher or professor. This work must be a compilation of accounts consulted, the different textual descriptions used and the image generated by Midjourney, with a brief description of the process. The mark obtained will also take into consideration the exhibition in the classroom.

Pros and cons of applying this activity in high school and university teaching

The use of new technologies applied in teaching, in this case Open Access AI, can be a dynamic incentive that breaks with the traditional master classes that are common in classical antiquity studies.

The main benefits of our approach can be seen in the different steps, as summarised in Table 1. The first stage deals with the search for and analysis of information in the literary sources. The student improves their skill in identifying the texts considered to be the most appealing to proceed with the study of the topic suggested by the teacher or professor. This benefits the student's initiative and autonomy. They themselves must research the literature and select the descriptions they consider most useful.

Table 1. Activity-benefit ratio of the approach

The second part of the activity is translation, focusing on the traditional approach to the ancient sources. The students must be able to translate texts. It is essential here that they are careful to use accurate vocabulary, since the varying meanings of certain terms and expressions can significatively alter the work of the AI, sometimes even making it impossible to process some idioms or sentences due to the use of irony or sarcasm. The students' skill in interpreting these sources will determine the degree of success of their activity.

The third phase encourages the students to understand the sources and to synthesise the description. Open Access AI does not usually allow long descriptions to be used, normally a maximum of four lines. This selection should not include rhetorical figures, meaning that the students need to understand the text, learning and embracing the information found in the literary sources and thus being able to delve into the historical content. Through this synthesisation, it is possible to develop certain abilities that will no doubt prove useful in the job market. The ability to process and select information, summarise it and reject superfluous elements is crucial.

The fourth and fifth steps deal with the interaction with Midjourney. The communication between the students and the AI helps facilitate the reprocessing of the data contained in the textual descriptions. The AI algorithm itself generates a representation based on the concepts contained in the command by establishing a sort of categorisation of the aforementioned concepts. For this reason, the images produced may focus more on certain elements than on others, which means the students have to reformulate the textual description to allow the AI to take into account some previously rejected details. This means of self-assessment has a clear aim: for the students to improve their information processing and presentation skills.

In the last stage, the students have to show their work to their classmates. This activity improves their oral and rhetorical skills. They need to explain the different steps they have undertaken, defending the results obtained by linking the images generated by Midjourney to the textual descriptions they prepared.

There are however some issues regarding the application of AI in the classroom. The main one is its accessibility, since the Deep Learning mechanism and the number of users of the software may have a powerful – and negative – influence on the results. For example, the iconography of an image about ancient Rome in the Republican period may contain Christian elements. The AI bases its depiction on what users ask for and is sometimes unable to avoid anachronisms. For this reason, it is essential for the students to be able to modify their textual descriptions to ensure that the representation prepared by Midjourney is as reliable as possible, although it may lose its visual appeal.

Another disadvantage is censorship. The programming of this sort of AI does not allow the use of certain expressions and terms that can be deemed discriminatory, derogatory or untrue within sensitive subjects. For example, terms such as ‘bust’, ‘phallus’ and ‘bacchanal’ are not taken into account by the AI due to their sexual connotations, whereas in the context of Classics they may not refer to situations related to that sphere.

Despite these disadvantages, we consider Midjourney, and Open Access AI in general, a very useful resource for an alternative way of teaching Classical Antiquity in both high schools and universities.

Conclusions

The increasing interest in new technologies in teaching is positive. However, it is important to be aware that these tools are not a way of replacing the teacher or professor, but of enhancing the teaching methodology. The activity proposed here aims to be an example of this reasoning. The versatility of AI provides an effective and dynamic resource for the learning process of the student body. The interaction between the software and the student enables both the correction of mistakes deriving from an erroneous translation and the skill of synthesising the content of the accounts. Likewise, the autonomy of the activity allows the students to develop their skills themselves, allowing them to select a topic, search for its literary references, translate and interpret the texts, and present them.

Footnotes

1 Apart from the university sphere, it is worth mentioning some educational applications in pedagogy: Ali and Abdel-Haq, Reference Ali, Abdel-Haq, Banu and Wood-Harper2020; Arshad Khan et al., Reference Arshad Khan, Khojah and Vivek2022; Chiu, Reference Chiu2021; Kit Ng et al., Reference Kit Ng, Luo, Yi Chan and Wah Chu2022; Toncic, Reference Toncic2020.

3 https://discord.com/ [last entry on 27 March 2023].

4 The bot must be downloaded from the official Midjourney website (https://www.midjourney.com/home/?callbackUrl=%2Fapp%2F [last entry on 27 March 2023]). Once it is downloaded, it will automatically be installed on the Discord server. In order to undertake the task, it is necessary to connect with the AI as a Discord contact; therefore only students who are invited to the conversation can use it.

5 In the case of high school teaching, where students may have less experience in working with the sources, the teacher should guide them and provide them with useful tools for compiling information from ancient literature. In this respect, a good tool would be the Perseus Project (http://www.perseus.tufts.edu/hopper/ [last entry on 27 March 2023]).

References

Ali, M and Abdel-Haq, MK (2020) Bibliographical analysis of artificial intelligence learning in higher education: is the role of the human educator and educated a thing of the past? In Banu, AM and Wood-Harper, T (eds), Fostering Communication and Learning With Underutilized Technologies in Higher Education. Hershey: IGI Global, pp. 3652.Google Scholar
Amato, G, Falchi, F and Vadicamo, L (2016) Visual recognition of ancient inscriptions using convolutional neural network and fisher vector. Journal on Computing and Cultural Heritage 9, 124.CrossRefGoogle Scholar
Argyrou, A and Agapiou, A (2022) A review of artificial intelligence and remote sensing for archaeological research. Remote Sensing 14, 123.CrossRefGoogle Scholar
Arshad Khan, M, Khojah, M and Vivek, V (2022) Artificial intelligence and big data: the advent of new pedagogy in the adaptive e-learning system in the higher educational institutions of Saudi Arabia. Education Research International 2022, 1263555. https://doi.org/10.1155/2022/1263555Google Scholar
Assael, Y, Sommerschield, TH, Shillingford, B, Bordbar, M, Pavlopoulos, J, Chatzipanagiotou, M, Androutsopoulos, I, Prag, J and de Freitas, N (2020) Restoring and attributing ancient texts using deep neural networks. Nature 603, 280283.CrossRefGoogle Scholar
Azmi, MS, Omar, K, Nasrudin, MF, Muda, AK and Abdullah, A (2011) Digital paleography: using the digital representation of Jawi manuscripts to support paleographic analysis. In 2011 International Conference on Pattern Analysis and Intelligence Robotics. Putrajaya: IEEE, pp. 71–77.CrossRefGoogle Scholar
Bengio, Y (2009) Learning deep architectures for AI. Foundations and Trends in Machine Learning 2, 1127.CrossRefGoogle Scholar
Bengio, Y, Lecun, Y and Hinton, G (2021) How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language? Communications of the ACM 64, 5865.CrossRefGoogle Scholar
Berganzo-Besga, I, Orengo, HA, Lumbreras, F, Carrero-Pazos, M, Fonte, J and Vilas-Estévez, B (2021) Hybrid MSRM-based deep learning and multitemporal sentinel 2-based machine learning algorithm detects near 10k archaeological tumuli in north-western Iberia. Remote Sensing 13, 4181.CrossRefGoogle Scholar
Caspari, G and Crespo, P (2019) Convolutional neural networks for archaeological site detection-finding ‘princely’ tombs. Journal of Archaeoogical Science 110, 104998.CrossRefGoogle Scholar
Chapinal-Heras, D and Díaz-Sánchez, C (2023) A Review of AI applications in Human Sciences research. Digital Applications in Archaeology and Cultural Heritage 30, e00288.Google Scholar
Chapinal-Heras, D, Rey-Álvarez, ME, Díaz-Sánchez, C, Pagola-Sánchez, L, Gómez-García, N and España-Chamorro, S (2023a) Historia y Arqueología en 3D. El proyecto ‘Experimenta la Antigüedad Digital’. In Bantim de Assumpçâo, LF, Fornis, C, da Costa Campos, CE and Ferreira Monteiro, A (eds), Encontros Transatlânticos: Diálogos em História, Patrimônio Cultural e Educação. Vassouras: Universidade de Vassouras, pp. 339362.Google Scholar
Chapinal-Heras, D, Díaz-Sánchez, C, España-Chamorro, S, Gómez-García, N, Pagola-Sánchez, L, Parada López De Corselas, M and Zafiria, Rey-Álvarez (2023b) Photogrammetry and 3D modelling in university teaching. A casestudy applied to the history degree. Journal of Classics Teaching 24, 133142.CrossRefGoogle Scholar
Chiu, WK (2021) Pedagogy of emerging technologies in chemical education during the era of digitalization and artificial intelligence: a systematic review. Education Sciences 11, 709. https://doi.org/10.3390/educsci11110709CrossRefGoogle Scholar
Cuéllar, A (2023) Cronología y estilometría: Datación automática de comedias de Lope de Vega. Anuario Lope de Vega 29, 97130.CrossRefGoogle Scholar
Cuéllar, A and Vega García-Luengos, G (2023) La Francesa Laura. El hallazgo de una nueva comedia del Lope de Vega último. Anuario Lope de Vega 29, 131198.CrossRefGoogle Scholar
Davis, DS, Gaspari, G, Lipo, CP and Sanger, MC (2021) Deep learning reveals extent of archaic native American shell-ring building practices. Journal of Archaeological Science 132, 105433.CrossRefGoogle Scholar
DiMaggio, P, Nag, M and Blei, D (2013) Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of U.S. Government arts funding. Poetic 41, 570606.CrossRefGoogle Scholar
Fahimirad, M (2018) A review on application of artificial intelligence in teaching and learning in educational context. International Journal of Learning and Development 8, 106118. https://doi.org/10.5296/ijld.v8i4.14057CrossRefGoogle Scholar
García-Serrano, A and Menta Garuz, A (2022) La inteligencia artificial en las Humanidades Digitales: Dos experiencias con corpus digitales. RHD 7, 1939.CrossRefGoogle Scholar
Garg, S and Sharma, S (2020) Impact of artificial intelligence in special need education to promote inclusive pedagogy. IJIET 10, 523527. https://doi.org/10.18178/ijiet.2020.10.7.1418CrossRefGoogle Scholar
Guan, C, Mou, J and Jiang, Z (2020) Artificial intelligence innovation in education: a twenty-year data-driven historical analysis. International Journal of Innovation Studies 4, 134147. https://doi.org/10.1016/j.ijis.2020.09.001CrossRefGoogle Scholar
Jarrahi, MH (2018) Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Business Horizon 61, 577586.CrossRefGoogle Scholar
Jia, S and Zhang, X (2021) Teaching mode of psychology and pedagogy in colleges and universities base on artificial intelligence technology. Journal of Physics: Conference Series 1852, 032033. https://doi.org/10.1088/1742-6596/1852/3/032033Google Scholar
Kang, K (2021) Restoring and mining the records of the Joseon Dynasty via neural language modeling and machine translation. ArXiv. abs/2104.05964.Google Scholar
Kit Ng, DT, Luo, W, Yi Chan, HM and Wah Chu, SK (2022) Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers and Education: Artificial Intelligence 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054Google Scholar
Köntges, TH (2020) Measuring philosophy in the first thousand years of Greek literature. Digital Classics Online 6, 123.Google Scholar
Kolve, E, Mottaghi, R, Han, W, Vander, Bilt E, Weihs, L, Herrasti, A, Deitke, M, Ehsani, K, Gordon, D, Zhu, Y, Kembhavi, A, Gupta, AK and Farhadi, A (2017) AI2-THOR: An interactive 3D environment for visual AI. ArXiv. abs/1712.05474.Google Scholar
Luo, J, Cao, Y and Barzilay, R (2019) Neural decipherment via minimum-cost flow: from Ugaritic to Linear B. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, pp. 3146–3155.CrossRefGoogle Scholar
Makridakis, S (2017) The forthcoming artificial intelligence (AI) revolution: its impact on society and firms. Futures 90, 4660.CrossRefGoogle Scholar
Meza-Lovn, GL (2012) A Graph-Based approach for transcribing ancient documents. In Ibero-American Conference on Artificial Intelligence. Cartagena de Indias: IBERAMIA, pp. 210–220.CrossRefGoogle Scholar
Nasimovna, NA (2022) New pedagogical technologies in teaching English language to students with no specialized foreign language. American Journal of Pediatrics Educational Research 6, 7679.Google Scholar
Orengo, HA and Garcia-Molsosa, A (2019) A brave new world for archaeological survey: automated machine learning-based potsherd detection using high-resolution drone imagery. Journal of Archaeological Science 112, 105013.CrossRefGoogle Scholar
Pagé-Perron, E, Sukhareva, M, Khait, I and Chiarcos, C (2017) Machine translation and auto-mated analysis of the Sumerian language. In Beatrice, N, Degaetano-Ortlieb, S, Kazantseva, A, Reiter, N and Szpakowiz, S (eds), Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Vancouver: Association for Computational Linguistics, pp. 1016.CrossRefGoogle Scholar
Panagopoulos, M, Papaodysseus, C, Roussopoulos, P, Dafi, D and Tracy, SV (2009) Automatic writer identification of ancient Greek inscriptions. Transections on Parameter Analysis and Machine Intelligence 31, 14041414.CrossRefGoogle ScholarPubMed
Ross, EAS (2023) A New Frontier: AI and Ancient Language Pedagogy. Journal of Classics Teaching 48(24), 143161.CrossRefGoogle Scholar
Roued-Cunliffe, H (2010) Towards a decision support system for reading ancient documents. Literary and Linguistic Computing 25, 365379.CrossRefGoogle Scholar
Schöch, C (2017) Topic modeling genre: an exploration of French classical and enlightenment drama. Digital Humanities Quarterly 11, number 2.Google Scholar
Sommerschield, T, Assael, Y, Pavlopoulos, J, Stefanak, V, Senior, A, Dyer, C, Bodel, J, Prag, J, Androutsopoulos, I and Freitas, N De (2023) Machine Learning for Ancient Languages: A Survey. Computational Linguistics, 145.Google Scholar
Toksha, B, Kulkarni, T and Gupta, P (2022) Impact of AI on teaching pedagogy and its integration for enhancing teaching-learning. In Churi, PP, Joshi, S, Elhoseny, M and Omrane, A (eds), Artificial Intelligence in Higher Education. Hershey: CRC Press, pp. 137152.CrossRefGoogle Scholar
Toncic, J (2020) Teachers, AI grammar checkers, and the newest literacies: emending writing pedagogy and assessment. Digital Culture and Education 12, 2651.Google Scholar
Tracy, SV, Papaodysseus, C, Roussopoulos, P, Panagopoulos, M, Fragoulis, D, Dafi, D and Panagopoulos, TH (2007) Identifying hands on ancient Athenian inscriptions: first steps towards a digital approach. Archaeometry 49, 749764.CrossRefGoogle Scholar
Tuzzi, A and Cortelazzo, M (2018) What is Elena Ferrante? A comparative analysis of a secretive bestselling Italian writer. Digital Scholarship in the Huumanities 33, 685702.CrossRefGoogle Scholar
Vaishya, R (2020) Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetology and Metabolic Syndrome 14, 337339.CrossRefGoogle ScholarPubMed
Wolf, L, Potikha, L, Dershowitz, N, Shweka, R and Choueka, Y (2011) Computerized paleography: tools for historical manuscripts. In 18th IEEE International Conference on Image Processing. Brussels: IEEE, pp. 3545–3548.CrossRefGoogle Scholar
Figure 0

Figure 1. Methodological approach. Stages and possibilities of the activity.

Figure 1

Table 1. Activity-benefit ratio of the approach