3
to mining engineering education. Specifically, in the last
three years, different workshops have taken place with the
objective of identifying emerging topics in mining engi-
neering education and to discuss the competencies and the
profile of the mining engineer of the future. These efforts
led to the identification of emerging topics that have gained
relevance in the years 2022, 2023, and 2024 (see Figure 1).
In 2022, the top emerging topics included automation,
artificial intelligence, entrepreneurship, diversity, and digi-
talization, reflecting a push towards technological advance-
ment and inclusivity. In 2023, the focus shifted to
sustainability, innovation, systems thinking, intergenera-
tional equity, and communication, highlighting the impor-
tance of sustainable practices and holistic approaches.
Meanwhile, in 2024, the emphasis was placed on artificial
intelligence, mine closure, sustainable mining, leadership,
and zero entry mining, indicating a strong orientation
towards responsible management, leadership development,
and the integration of cutting-edge technologies in mining
operations. This evolving landscape of topics underscores
the dynamic nature of the mining industry and highlights
the multifaceted skill set required for future mining engi-
neers. As the industry evolves, it’s clear that a comprehen-
sive approach, integrating technological innovation with
sustainable and ethical practices, will be crucial.
In 2023, a workshop was conducted to pinpoint the
technical and interpersonal skills needed to drive transfor-
mation in the mining industry. During this workshop, dis-
cussions highlighted five key technical skills: environmental
awareness, comprehensive technical expertise in mining,
programming, project management, and smart data and
big data management. On the interpersonal front, the top
competencies identified were communication, leadership,
social awareness, emotional intelligence, and community
engagement. These insights supported a joint vision of the
mining engineer of the future. This professional is envi-
sioned as a driven individual and a competent engineer
with curiosity and creativity who applies general technical
Figure 1. Top 5 Emerging Topics in Mining Engineering
Education
mining skills in a globalized world, who fully understands
the mineral value chain and its relevance for modern life,
and who can work in a multidisciplinary team environment
with the major goal of enhancing and driving the sustain-
able extraction of raw materials for future generations.
Moreover, these insights have enabled the identifica-
tion of four streams that are redefining mining engineering
education: artificial intelligence, sustainability and ESG,
future technologies and generational shifts.
Artificial Intelligence
Firstly, the integration of artificial intelligence (AI), namely
generative AI emerges as a key focus, transforming student
engagement and content delivery, yet this enthusiasm is
often tempered by the need for rigorous analysis and ethical
considerations. In higher education, the use of AI can sup-
port the development of collaborative content and projects,
however, it’s use by students and academics brings its own
set of concerns, particularly around academic integrity and
the possible impact in the learning process. This paradigm
has led to higher institutions having different perspectives
towards the use and implementation of AI. Depending
on the accessibility to technological infrastructure and
resources, some institutions embrace the integration of AI
in teaching and learning, as well as into their administra-
tive processes. Conversely, other institutions approach AI
adoption with a more cautious perspective. Developing
student facilities with AI must include an understanding
not only of the underlying data structure and data manage-
ment practices, but also specific applicability and limita-
tions of AI. For instance, gathering, synthesis and analysis
of geological data and how it can be leveraged by AI-guided
mineral exploration (2). Algorithms developed on specifi-
cally formatted data designed for use in this application will
‘learn’ over time through refinement and human inputs.
This refinement process is critical to improving the accuracy
and usability of the tool. This implies a continuing need to
interact with the autonomously generated data to ensure
the conclusions drawn are appropriate. While introducing
students to the potential offered by AI, academics also have
to train them on the limitations of the technology. Critical
evaluation skills have to go hand-in-hand with the use of
these technologies to ensure outputs correctly reflect and
interpret the underlying data and students learn to quantify
and qualify the conclusions they draw from these tools.
Generative AI (gen-AI) is one of the most diverse tools
in the AI arsenal. It presents an ongoing challenge for aca-
demics as they work to find the optimal balance between
educating students on the current and future applications of
AI and the threat it presents to academic integrity. Mining
to mining engineering education. Specifically, in the last
three years, different workshops have taken place with the
objective of identifying emerging topics in mining engi-
neering education and to discuss the competencies and the
profile of the mining engineer of the future. These efforts
led to the identification of emerging topics that have gained
relevance in the years 2022, 2023, and 2024 (see Figure 1).
In 2022, the top emerging topics included automation,
artificial intelligence, entrepreneurship, diversity, and digi-
talization, reflecting a push towards technological advance-
ment and inclusivity. In 2023, the focus shifted to
sustainability, innovation, systems thinking, intergenera-
tional equity, and communication, highlighting the impor-
tance of sustainable practices and holistic approaches.
Meanwhile, in 2024, the emphasis was placed on artificial
intelligence, mine closure, sustainable mining, leadership,
and zero entry mining, indicating a strong orientation
towards responsible management, leadership development,
and the integration of cutting-edge technologies in mining
operations. This evolving landscape of topics underscores
the dynamic nature of the mining industry and highlights
the multifaceted skill set required for future mining engi-
neers. As the industry evolves, it’s clear that a comprehen-
sive approach, integrating technological innovation with
sustainable and ethical practices, will be crucial.
In 2023, a workshop was conducted to pinpoint the
technical and interpersonal skills needed to drive transfor-
mation in the mining industry. During this workshop, dis-
cussions highlighted five key technical skills: environmental
awareness, comprehensive technical expertise in mining,
programming, project management, and smart data and
big data management. On the interpersonal front, the top
competencies identified were communication, leadership,
social awareness, emotional intelligence, and community
engagement. These insights supported a joint vision of the
mining engineer of the future. This professional is envi-
sioned as a driven individual and a competent engineer
with curiosity and creativity who applies general technical
Figure 1. Top 5 Emerging Topics in Mining Engineering
Education
mining skills in a globalized world, who fully understands
the mineral value chain and its relevance for modern life,
and who can work in a multidisciplinary team environment
with the major goal of enhancing and driving the sustain-
able extraction of raw materials for future generations.
Moreover, these insights have enabled the identifica-
tion of four streams that are redefining mining engineering
education: artificial intelligence, sustainability and ESG,
future technologies and generational shifts.
Artificial Intelligence
Firstly, the integration of artificial intelligence (AI), namely
generative AI emerges as a key focus, transforming student
engagement and content delivery, yet this enthusiasm is
often tempered by the need for rigorous analysis and ethical
considerations. In higher education, the use of AI can sup-
port the development of collaborative content and projects,
however, it’s use by students and academics brings its own
set of concerns, particularly around academic integrity and
the possible impact in the learning process. This paradigm
has led to higher institutions having different perspectives
towards the use and implementation of AI. Depending
on the accessibility to technological infrastructure and
resources, some institutions embrace the integration of AI
in teaching and learning, as well as into their administra-
tive processes. Conversely, other institutions approach AI
adoption with a more cautious perspective. Developing
student facilities with AI must include an understanding
not only of the underlying data structure and data manage-
ment practices, but also specific applicability and limita-
tions of AI. For instance, gathering, synthesis and analysis
of geological data and how it can be leveraged by AI-guided
mineral exploration (2). Algorithms developed on specifi-
cally formatted data designed for use in this application will
‘learn’ over time through refinement and human inputs.
This refinement process is critical to improving the accuracy
and usability of the tool. This implies a continuing need to
interact with the autonomously generated data to ensure
the conclusions drawn are appropriate. While introducing
students to the potential offered by AI, academics also have
to train them on the limitations of the technology. Critical
evaluation skills have to go hand-in-hand with the use of
these technologies to ensure outputs correctly reflect and
interpret the underlying data and students learn to quantify
and qualify the conclusions they draw from these tools.
Generative AI (gen-AI) is one of the most diverse tools
in the AI arsenal. It presents an ongoing challenge for aca-
demics as they work to find the optimal balance between
educating students on the current and future applications of
AI and the threat it presents to academic integrity. Mining